Importing_Data_in_Python_part1.ipynb

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   "source": [
    "# Importing Data in Python (Part 1)\n",
    "\n",
    "As a Data Scientist, on a daily basis you will need to clean data, wrangle and munge it, visualize it, build predictive models and interpret these models. Before doing any of these, however, you will need to know how to get data into Python. In this course, you'll learn the many ways to import data into Python: (i) from flat files such as .txts and .csvs; (ii) from files native to other software such as Excel spreadsheets, Stata, SAS and MATLAB files; (iii) from relational databases such as SQLite & PostgreSQL.\n",
    "\n",
    "## Import Data From Plain Text\n",
    "To check out any plain text file, you can use the Python's open() function to open a connection to the file.\n",
    "```python\n",
    "#assign the file name to the variable string\n",
    "file_name = 'file_name.txt'\n",
    "#pass the file name to the open function using 'r' mode \n",
    "file = open(file_name, mode='r')# only for reading\n",
    "#to connection to the file apply the read() function\n",
    "text =file.read()\n",
    "#close the connection\n",
    "file.close()\n",
    "\n",
    "```\n",
    "By using a context manager construct that allows us to create a context in which you can execute commands with the file open. We can avoid having to close to connection to the file using the 'with' statement. For the large files, we may want to print a few lines. You can use 'file.readline()' function to execute the first line of a text file. If you execute the same comment again, the second line will be printing and so on.\n",
    "```python\n",
    "#By using the with open we don't need to close the connection.\n",
    "with open(file_name, 'r') as file:\n",
    "     print(file.read())\n",
    "#print out the file line by line executing with file.readline()\n",
    "with open(file_name, 'r') as file:\n",
    "     print(file.readline())\n",
    "     print(file.readline())\n",
    "     print(file.readline())\n",
    "```\n",
    "## Import data from Flat File\n",
    "\n",
    "Flat files are basic text file  containing records, that is table data, without structured relationships.\n",
    "\n",
    "It is also essential to know flat files can have a header such as in’titanic.csv’, which is row that occurs as the first row and describes the content of data columns or states what the corresponding attributes  or features in each column are.\n",
    "### importing flat file uisng Numpy\n",
    "We're now going to load the MNIST digit recognition dataset using the numpy function `loadtxt()` and see just how easy it can be:\n",
    "* The first argument will be the `filename`.\n",
    "* The second will be the `delimiter` which can take `','` for comma separated file and `'t'` for tab-delimated file\n",
    "* `skiprows` allows you to specify how many rows you wish to skip.\n",
    "* `usecols` takes a list of the indices of the columns you wish to keep.\n",
    "* `dtype=’str’` will ensure that all entries are imported as strings."
   ]
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   "cell_type": "code",
   "execution_count": 7,
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     "text": [
      "<class 'numpy.ndarray'>\n",
      "[[1. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [1. 0. 0. ... 0. 0. 0.]\n",
      " ...\n",
      " [2. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [5. 0. 0. ... 0. 0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "import numpy as np\n",
    "file_name = 'data/mnist_kaggle_some_rows.csv'\n",
    "#Default delimeter is white space so, we need the specify delimeter parameter explicity.\n",
    "data = np.loadtxt(file_name, delimiter=',')\n",
    "#we are reading file as a numpy array\n",
    "print(type(data))\n",
    "print(data)"
   ]
  },
  {
   "attachments": {},
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   "source": [
    "**Importing different datatypes:** \n",
    "Let's imprt the text file which has\n",
    "* text header, consisting of  string\n",
    "* tab-delimated\n",
    "\n",
    "We are going to use seaslug.txt file which the data consists of percentage of sea slug larvae that had metamorphosed in a given time period. \n",
    "\n",
    "Due to the file  containing `string` header we need to handle it. if we import the file uisng loadtxt() without handling string file we get the `ValueError` which is tell us `could not convert string to float`. We can hadle it in 2 way:\n",
    "\n",
    "Alternative 1: We can set the `dtype =str` to avoid to ValueError,<br>\n",
    "Alternative 2: We can skip the first row , using the skiprows argument!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n",
      "[['Time' 'Percent']\n",
      " ['99' '0.067']\n",
      " ['99' '0.133']\n",
      " ['99' '0.067']\n",
      " ['99' '0']]\n"
     ]
    }
   ],
   "source": [
    "#Alternative 1:\n",
    "file_sea_slug = 'data/seaslug.txt'\n",
    "data_alt_1 = np.loadtxt(file_sea_slug, delimiter='\\t', dtype=str)\n",
    "print(type(data_alt_1))\n",
    "print(data_alt_1[0:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n",
      "[[9.90e+01 6.70e-02]\n",
      " [9.90e+01 1.33e-01]\n",
      " [9.90e+01 6.70e-02]\n",
      " [9.90e+01 0.00e+00]\n",
      " [9.90e+01 0.00e+00]\n",
      " [0.00e+00 5.00e-01]\n",
      " [0.00e+00 4.67e-01]\n",
      " [0.00e+00 8.57e-01]\n",
      " [0.00e+00 5.00e-01]]\n"
     ]
    }
   ],
   "source": [
    "#Alternative 2:\n",
    "data_alt_2 = np.loadtxt(file_sea_slug, delimiter='\\t', skiprows=1)\n",
    "print(type(data_alt_2))\n",
    "print(data_alt_2[0:9])"
   ]
  },
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   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To import datasets which have different datatypes in different columns;for example, one column may contain strings and another floats. The function `np.loadtxt()` will freak at this. There is another function, `np.genfromtxt()`, which can handle such structures. If we pass `dtype=None` to it, it will figure out what types each column should be.\n",
    "```python\n",
    "data = np.genfromtxt(file_name, delimiter=',', names=True, dtype=None)\n",
    "```\n",
    "* The first argument is the filename\n",
    "* the second specifies the delimiter\n",
    "* The third argument names tells us there is a header (names=True)\n",
    "* dtype=None is represent the different data type\n",
    "\n",
    "*There is also another function `np.recfromcsv()`that behaves similarly to np.genfromtxt(), except that its default dtype is None!*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(1, 0, 3, b'male', 22., 1, 0, b'A/5 21171',  7.25  , b'', b'S')\n",
      " (2, 1, 1, b'female', 38., 1, 0, b'PC 17599', 71.2833, b'C85', b'C')]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:5: VisibleDeprecationWarning: Reading unicode strings without specifying the encoding argument is deprecated. Set the encoding, use None for the system default.\n",
      "  \"\"\"\n"
     ]
    }
   ],
   "source": [
    "# Let's look at titanic.csv \n",
    "# ',', comma separated\n",
    "# there is header\n",
    "# there are string and number data type in it.\n",
    "titanic = np.genfromtxt('data/titanic_sub.csv', delimiter=',', names=True, dtype=None)\n",
    "print(titanic[0:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(1, 0, 3, b'male', 22., 1, 0, b'A/5 21171',  7.25  , b'', b'S')\n",
      " (2, 1, 1, b'female', 38., 1, 0, b'PC 17599', 71.2833, b'C85', b'C')\n",
      " (3, 1, 3, b'female', 26., 0, 0, b'STON/O2. 3101282',  7.925 , b'', b'S')\n",
      " (4, 1, 1, b'female', 35., 1, 0, b'113803', 53.1   , b'C123', b'S')\n",
      " (5, 0, 3, b'male', 35., 0, 0, b'373450',  8.05  , b'', b'S')]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/numpy/lib/npyio.py:2278: VisibleDeprecationWarning: Reading unicode strings without specifying the encoding argument is deprecated. Set the encoding, use None for the system default.\n",
      "  output = genfromtxt(fname, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "# using np.recfromcsv()\n",
    "titanic_2 = np.recfromcsv('data/titanic_sub.csv')\n",
    "print(titanic_2[0:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing flat files using pandas\n",
    "\n",
    "What we learn so far is to import a bunch of different types of flat files into python as Numpy arrays. Although the numpy array is incredibly useful and has numerous of purpose, they can not handle the data as two dimensional labeled data structure.\n",
    "Pandas offer us to the DataFrame, which has observations (rows), and variables(columns)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
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       "      <td>S</td>\n",
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       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
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       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
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       "      <th>4</th>\n",
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       "      <td>0</td>\n",
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       "      <td>8.0500</td>\n",
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       "      <td>S</td>\n",
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       "   PassengerId  Survived  Pclass     Sex   Age  SibSp  Parch  \\\n",
       "0            1         0       3    male  22.0      1      0   \n",
       "1            2         1       1  female  38.0      1      0   \n",
       "2            3         1       3  female  26.0      0      0   \n",
       "3            4         1       1  female  35.0      1      0   \n",
       "4            5         0       3    male  35.0      0      0   \n",
       "\n",
       "             Ticket     Fare Cabin Embarked  \n",
       "0         A/5 21171   7.2500   NaN        S  \n",
       "1          PC 17599  71.2833   C85        C  \n",
       "2  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3            113803  53.1000  C123        S  \n",
       "4            373450   8.0500   NaN        S  "
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     "execution_count": 46,
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   ],
   "source": [
    "df_titanic = pd.read_csv('data/titanic_sub.csv')\n",
    "\n",
    "print(type(df_titanic))\n",
    "df_titanic.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Handling read_csv() function under different circumstances \n",
    "* `sep:` Stands for a separator, a default is ',' as in .csv(comma separated values): We could specify if needed.\n",
    "*` header= None:` Load a CSV with no headers\n",
    "* `names=['column_name1','column_name2']:` Load a .csv while specifying column names\n",
    "* `index_col='date':` Load a .csv with setting the index column to column name like we would like set index as a date.\n",
    "* `na_values=['NA']:` Load a .csv while specifying \"NA\" as missing values.\n",
    "* `skiprows=3:` Load a .csv while skipping the top 3 rows\n",
    "\n",
    "**Pickle file**: <br>\n",
    "**Warning Loading pickled data received from untrusted sources can be unsafe**\n",
    "Let's create the pickle file and then load it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import pickle\n",
    "import pickle\n",
    "#Let's save the titanic data frame into picle file\n",
    "pd.to_pickle(df_titanic, 'data/titanic_pickle.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "   PassengerId  Survived  Pclass     Sex   Age  SibSp  Parch  \\\n",
      "0            1         0       3    male  22.0      1      0   \n",
      "1            2         1       1  female  38.0      1      0   \n",
      "2            3         1       3  female  26.0      0      0   \n",
      "\n",
      "             Ticket     Fare Cabin Embarked  \n",
      "0         A/5 21171   7.2500   NaN        S  \n",
      "1          PC 17599  71.2833   C85        C  \n",
      "2  STON/O2. 3101282   7.9250   NaN        S  \n"
     ]
    }
   ],
   "source": [
    "#first try with with open\n",
    "with open ('data/titanic_pickle.pkl','rb') as file:\n",
    "    data = pickle.load(file)\n",
    "#reading the data type as DataFrame\n",
    "print(type(data))\n",
    "print(data[0:3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
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       "      <td>PC 17599</td>\n",
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       "      <td>C85</td>\n",
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       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "   PassengerId  Survived  Pclass     Sex   Age  SibSp  Parch  \\\n",
       "0            1         0       3    male  22.0      1      0   \n",
       "1            2         1       1  female  38.0      1      0   \n",
       "2            3         1       3  female  26.0      0      0   \n",
       "3            4         1       1  female  35.0      1      0   \n",
       "4            5         0       3    male  35.0      0      0   \n",
       "\n",
       "             Ticket     Fare Cabin Embarked  \n",
       "0         A/5 21171   7.2500   NaN        S  \n",
       "1          PC 17599  71.2833   C85        C  \n",
       "2  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3            113803  53.1000  C123        S  \n",
       "4            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's read with pandas\n",
    "unpickled_df = pd.read_pickle('data/titanic_pickle.pkl')\n",
    "unpickled_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Excel File:** \n",
    "You may need to deal with excel file at some point. Here, you'll learn how to use pandas to import Excel spreadsheets. \n",
    "* Use the pandas's ExcelFile to load the .xlsx  file \n",
    "* Figure out the what is the sheets name using `.sheet_names`\n",
    "* we could load the particular sheets into a dataframe, we need to apply a parse() method with a single parameter, which is either the name of string or the index as a float of the sheets that you wish to load.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['2002', '2004']\n"
     ]
    }
   ],
   "source": [
    " excel_data = pd.ExcelFile('data/battledeath.xlsx')\n",
    " print(excel_data.sheet_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  War(country)      2004\n",
      "0  Afghanistan  9.451028\n",
      "1      Albania  0.130354\n",
      "2      Algeria  3.407277\n",
      "3      Andorra  0.000000\n",
      "4       Angola  2.597931\n"
     ]
    }
   ],
   "source": [
    "#Load the sheet into dataframe \n",
    "df1 = excel_data.parse('2004')\n",
    "print(df1.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>War, age-adjusted mortality due to</th>\n",
       "      <th>2002</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>36.083990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>0.128908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>18.314120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>18.964560</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  War, age-adjusted mortality due to       2002\n",
       "0                        Afghanistan  36.083990\n",
       "1                            Albania   0.128908\n",
       "2                            Algeria  18.314120\n",
       "3                            Andorra   0.000000\n",
       "4                             Angola  18.964560"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = excel_data.parse('2002')\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2004</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>War(country)</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>9.451028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>0.130354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>3.407277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>2.597931</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  2004\n",
       "War(country)          \n",
       "Afghanistan   9.451028\n",
       "Albania       0.130354\n",
       "Algeria       3.407277\n",
       "Andorra       0.000000\n",
       "Angola        2.597931"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "excel_df = pd.read_excel('data/battledeath.xlsx', index_col=0, sheet_name='2004')\n",
    "excel_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**SAS File:** Statistical analysis System\n",
    "* Business analysis and biostatistics\n",
    "* Advanced analysis, Multivariable analysis\n",
    "* Data management\n",
    "* Predictive analytics\n",
    "* sas7bdat: dataset files & sas7cdat: catalog file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     YEAR     P           S\n",
      "0  1950.0  12.9  181.899994\n",
      "1  1951.0  11.9  245.000000\n",
      "2  1952.0  10.7  250.199997\n",
      "3  1953.0  11.3  265.899994\n",
      "4  1954.0  11.2  248.500000\n"
     ]
    }
   ],
   "source": [
    "#pip install sas7bdat\n",
    "# import the sas7bdat \n",
    "from sas7bdat import SAS7BDAT\n",
    "with SAS7BDAT ('data/sales.sas7bdat') as file:\n",
    "    df_sas = file.to_data_frame()\n",
    "print(df_sas.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Stata File: \"Statistics\" + \"data\"**\n",
    "* Importing Stata files as DataFrames using the pd.read_stata()from pandas.\n",
    "* Academic & social sciences research"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  wbcode               country  disa1  disa2  disa3  disa4  disa5  disa6  \\\n",
      "0    AFG           Afghanistan   0.00   0.00   0.76   0.73    0.0   0.00   \n",
      "1    AGO                Angola   0.32   0.02   0.56   0.00    0.0   0.00   \n",
      "2    ALB               Albania   0.00   0.00   0.02   0.00    0.0   0.00   \n",
      "3    ARE  United Arab Emirates   0.00   0.00   0.00   0.00    0.0   0.00   \n",
      "4    ARG             Argentina   0.00   0.24   0.24   0.00    0.0   0.23   \n",
      "\n",
      "   disa7  disa8   ...    disa16  disa17  disa18  disa19  disa20  disa21  \\\n",
      "0   0.00    0.0   ...       0.0     0.0     0.0    0.00    0.00     0.0   \n",
      "1   0.56    0.0   ...       0.0     0.4     0.0    0.61    0.00     0.0   \n",
      "2   0.00    0.0   ...       0.0     0.0     0.0    0.00    0.00     0.0   \n",
      "3   0.00    0.0   ...       0.0     0.0     0.0    0.00    0.00     0.0   \n",
      "4   0.00    0.0   ...       0.0     0.0     0.0    0.00    0.05     0.0   \n",
      "\n",
      "   disa22  disa23  disa24  disa25  \n",
      "0    0.00    0.02    0.00    0.00  \n",
      "1    0.99    0.98    0.61    0.00  \n",
      "2    0.00    0.00    0.00    0.16  \n",
      "3    0.00    0.00    0.00    0.00  \n",
      "4    0.00    0.01    0.00    0.11  \n",
      "\n",
      "[5 rows x 27 columns]\n"
     ]
    }
   ],
   "source": [
    "df_stata = pd.read_stata('data/disarea.dta')\n",
    "print(df_stata.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**HDF5: Hierarchcal Data Format version 5**\n",
    "* Storing for large quantitie of numerical data\n",
    "* Datasets can be  hundred of gigabytes, or terabytes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'h5py._hl.files.File'>\n"
     ]
    }
   ],
   "source": [
    "import h5py\n",
    "df_h5py = h5py.File('data/L-L1_LOSC_4_V1-1126259446-32.hdf5', 'r')\n",
    "print(type(df_h5py))\n",
    "print(df_h5py)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "meta\n",
      "quality\n",
      "strain\n"
     ]
    }
   ],
   "source": [
    "for key in df_h5py.keys():\n",
    "    print(key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'h5py._hl.group.Group'>\n"
     ]
    }
   ],
   "source": [
    "print(type(df_h5py['meta']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Description\n",
      "DescriptionURL\n",
      "Detector\n",
      "Duration\n",
      "GPSstart\n",
      "Observatory\n",
      "Type\n",
      "UTCstart\n"
     ]
    }
   ],
   "source": [
    "for key in df_h5py['meta'].keys():\n",
    "    print(key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b'L1'\n"
     ]
    }
   ],
   "source": [
    "print(df_h5py['meta']['Detector'].value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EjOrz0O3/WzaN56oKYnHOlbCd4cgRKqPtLDOUN0XUU7ZhPMce2qPeto3b9M55fgJGXET8mXZqjHTnSGYcpjUg/IhROPt3aBGyZzRiaHT7Fk3QuXXTWMdLg0aSdScTmmnMIAGgc+tmAICubZtZ+f5KwRmOhKm7ahwu/dtbSvv67cagrq3r7fPrBFxMqqyMkGcQ5bNNkUUn9ZFEMYms9C30n/q7V2K3Iwzxd9LJsbl53Ox62yZbmKUBwFMzyhdZbxp7UMCe0YhCm00aNcBVZw4qez+PJXRtaaaZ1rz5wsg67c+ERT3mHWc4DFHJSp04txAjHuTH9OP3TcuiiVTDu5cmMNXdtjP5YkWyGf+E2eEyF6+lLJAouqpuPGeI8udkP913/jPNQouAXT4NqS5tzEfA/ix4/yj8X74M7Txw01PvZ/r9JpI5Y//0egItSQdnOAzxP6gyvvj3t7WOuSHCFQSo5zREzQ5M+LlkxGwbmaz8nS/bXyh8cMoy1F01zsj1J5YKbd5E3bWRZFKXJU8NAGDSwvJZkP8cN2/PX7XDrQGDGn9lyaQwMRyVXMPdGQ5DdkkW+MJCXFVkM/77tr1EQlniVVw2ShZ3zx7WPfZxxYgYk6jG2yfou8h+VFQIPvk3E/W/sAI4qo8ksMKQfp3KAxb+81ayCa8mLN9Q6oTFezKt3NG8h8/aJhPDQUQXEtEsItpLRMND9mtHRA8T0Rwimk1EI9NsZxh3S7J+RYkDv7zy4vXp5k08lkDm6UKf+N11YwbjsuP6xj7ujOUlSQxx5PbVY/vIdq+HbOQWNpMQR6EbFWZ5SXP5/e/giJ9PsFqPu22LeIl7lcyvLxy677Vq8MIx/eMZWlXlAA8bEYhZktWMYyaA8wBErWLeDuAZZh4EYBiA5H0lisjcSmL28HbfGshjAYk+Iir3uGplM9lI60bfgmnc2PdPHdoDB/dsG+sYQPksQwxrbNWskWRvNZ4MMZx5Kxr11IxVWLtlBw7+aT7L+lYaYt6Tao3ztLWhLrlnsnT7DWcfmGo7TMnEcDDzbGYODQ8iojYAjgfw1+JndjKzmcBSAsgqof3k8VJi0/Sl5U19ZlZ0ElbY+sXQYgctTsl18Ud+xO1AO7ayE6YpjtVsyYv89H/Bi6W/GG9v/NG/k928E9MAhNfm56+KXBL5GrqoRoDFvZd11ziCZkKi0dPlG/+aij5XjzP+vA55XuPoC2AtgL8T0TQiuoeIAuMyiegyIppCRFPWrrUv/+Fn3IxVoe/LpBaiEDuNVk3NR9syHvlmwct3x8WH7duWhAaUSRW2IJKovw7UrzcRp3a6bZfQw1PNVHOTUNs9qHubWJ/3hwirsmcv4+an3reSMBlWYEt83jyxwiiCcplk+mUmDOtlPoN/Ztbq9NZ0kjowEU0gopmSf2MVD9EIwGEA/szMhwLYCuCqoJ2Z+S5mHs7Mwzt16mThDOLx4BT9B/nuV0uid7ZFEw8vZhOPPrhUTTAJYUaThz2olkda9a3jzOJsc/0TsxI57moDXSwdORUZYd6fMGN92E3P457XFmHYz5J13a3bUloHa9RArSuU6ZTZ5KDu8V2/aZCY4WDmUcw8RPLvCcVDLAewnJk9Z+DDKBiSmuCrvkVnW6VVxVnGH19cYOWYIibhj0Hhtt4z2iymXlIUUV1BHlwucTHRyYrbSYbdsS+FiIKmJc0iTrhNznVAFzM35YYqqE+fW1cVM68GsIyIBhY3nQIg2yyfFDl+QMeyv68ZPdj4WF88uk66Xadqn/9hPk+QBvmpsKD3sydNRszyLsZLRFMdDZoyI6A4lce/J5US3po2Cm6LP5LOlLQ6zijEvjTuwOX6s8oXfVXzkVRmxaauxjLDsVftGOJV2K2QyyVj2jL5c/er84eW/a3rETjtwC7GStq6ZBWOey4RLQcwEsA4Inq2uL07EY0Xdv0OgPuIaAaAQwD8Iv3WZoP/nmnTzNyX/vmRvWO2Bljhc+eIMiAH9ShNr03qUgQttXjXwKsOqItM40oW8voPQcBQhljFLuxRDgv9XLBGXT8rCRVdk6ihUwZ13vf6Wt/ARUVG/o6XSjNafya7qhlSkZ5fubG0TxuNSDwxhLZnezV9L9Hgmc7I/vGGPPPeL1/z6ny94JXn3v8wNS27rKKqHmPmnszclJm7MPPpxe0rmXm0sN/04rrFUGb+FDPbkcBMGBu6OX6VWJ0H3+/P9idwmeDv3MUIrSPq4qmxBg1m41b+e2lufakS3QptfsKCFsKau32X+qj4mkff02lSGYO7yRe0TWT0vap2AHCgb6FcxbgtFBJiTxxYvu4YpLzgL/c6/r3wIBSg/N4877Cekft7iM+UKKsSNoP5hyC3YjrjCJIg8qsQp5X1bkJuXVWVyic791hZ1G3XonzKqdOJRtXFuPio+nW9o/A/JDajifzFdDwuOFy9E5Ahi3z788T66zprI8TmRBfUL84N1qayVeJ2YYwiWxcf2Uu63US5OOxsdCvUtfQZ3CBtKX+FyX9Nko/Og/CMiN/tI+OPL8oVB8IUIJZvKGnAnXOIXDVBV83Zo5fPcNha10wCZzgsM33ZxsAbMg67NWYcfpE6P3UGstu/fT59Vd6+CjOlMD+wTBxR1imLpUBl/Ob5efte+w16WVtCj5ISwvB7RN94M0Hx0h7Sq13Ze78VrokJQSWI75tcLmeiolb7rrBGdfpBhajBzm2i8zK8aLoj6tqXbQ8LUxdDxMcc3E26j61cQt1s9DRxhsOAsM5qz17GvyfZ1/LRcVVFDXxHB9zwYWQRsqry2EzRLODjl02xia0Zhy3GDI2rIyZ35SSJimvKj6jWO7hrvNwTIFwvLWpQBljMQs+v3XCGw4Sw/uGWp4OzkuNEy/hHfGFE1Vr2is/kHZUBl606DKqEjQLfWhisP3TfZD2Xi5UcG4NjiN8bR5rdFJPTFj/jdewDJbVsbJCmNMkDORST9HCGQ+Azf3kTtz0bXTtcvHVu/lS5zzvMP/pujKxqHX/ng1NKyrhXnlY/I9akzKVfeysNksocj8PhvdsHvvfB2uCF91fm6UXIhB0rjL5CtJtJFyf2i61jRPLJkBUms4E40/MyuLu1jS6w5T1T/oX6MOP1xPSVkfupzEpUeHZW+ABQJO2iUM5wCExe9FGoRIGHeKP6o022pTwCliE+/IdJOroGBoYjah3ABt0roPxmmAHXWYeKYvUms47gmP6l/B8Tl2SSI+pvntgvkeOK7kr/IjwQPJjzch5a+0J4VWd7HVsHrHeFfPz9ldHRaEF5V2GYVi40xRkOA0TDcdj+wSNQP2mtdYk3/tH9OobsmS9G9C2Xtla5XlHuv7cXl9xHcdWAo1BNAPzacdFy8TePi5/raiLcl+Q6jak2WtzEyqBaOOs/LkQ/XX1meY7KOMV1lqBZTVhzg3KSfi5E67Vpnn9JfGc4DNi+0zBTVdH1EpadDERLYKz7ONkkoFMP7FJvmygSZxx/btCvXPXIjND3xXobSUuHqCaEDVfIe9mSUZW9JGccpuOmuNInQcbQKznsn3FMDJFDifN9QHCOSLeYs+20A7Cc4TDgb68vit5JgqrvMyoMT6XeuSomvvSe7euPtFoI5UVnrthU730VTNY0tka4Bl+eVwrJTbozDhsZi8Wmju0fPQvMqqxoWKd3spBJbsLR/cyKJcXtyKNmmv7HLa7xDPv8LU/L11BPGBDv2qaNMxwG6CY/efz6ObXY96glCJtjQpNIr2+eUN9XLfr+dUY/ouHyn7cNr4kYGv2nl4Lza+JIq3vnrrrG0YAodJE9KVQ6xLAM94uP1E8cFelgWPPinaXxBCOiQrD999l7hgMfj7DrPD9AekZ8fi40SHxNQuk6DGc4DDCNmlCNqhraMzz01uY90tbAnyqLtjnuAFFSQt1yiIbLv7DZJMJlp4uYoT7Wl/UbJ1Ta6yj+38TowAoAaN6kIa4+c5Dx95kyTaED/utrwbPp9j4BvalL1Mqf3uSrPBmEOIg4YUDpftqhIdci4/YXwhNyVcvLRuG1+Z7X6peV9lCZVaskL/r5y8vB35kEznCY4Ou4TwmZwrdsop84dcWoA7S+Pw628iBEba1Nn6ivsTQUpiff8kXd2JZceP79Unjjd0eVhymnnbpno+RuEmwNEZT0z5LWK66lNVVMHhS/WyxzbOoaVsUkPF2GN/gIi3D6RGH9r6HBgsWkhelWgHSGwwB/yG2/zsHSGH7FyyBE9dSoDtNWnDgQXAsjjOYRxvDax2aGvi8iLnyqdjA28C9GxpnFfemYOu3PqJQIVXU/zFmtpqYbJCwoohNSbHsdXVyL8Av+BSG6hboYjNQBoHeHwMKiWngGSHeN5PMjytWr86xR5eEMhwF+4bWwEpvXn6U2TX93WcmvGnXb2HBVefo8SSQO6eSy/Oa5kgZWUpEhsg7YpoTGyL5mi77+5FE/QXpOfsJmCSK/fCY6uVW1LgWgLv/esolcUdgvxnmrsHCsGrorGht/OLcuOuoMMrz8KN2Q5pt894FJ2HISZaDDcIbDAmcM6Rr4nmr1OvFmi7oHVCOhRFlsP96oZlKITIbHmi16NTZUpuMei9eVpvVJ3fpBelaiG9FU0RQwHyFe4htp+lHvf9S+X6UevM5gOWztYL6gxntmwPMx36fYK9bVUKXsudH+dDlxR/qei0nD9mLxrWNifaeH6uDBFs5wJIzqNFh8AKI+89+3l4W+7xGWia2jvKm7OKmTL2F7Wn7GQfU7qbtfkS8cikEIv5tgrvYaR8V08a1jsPjWMXjkm0fXe2+WQpaxbWwlAJZFywX8xn4jZRIZJB7j8Jh1YX58RnjAQlQioleoUvUaPnDZCKX9VLCpWqCCMxwx8KJ+wkqbdmrdFGcPi1YpFX/4qIxf1X4qrEP7zskRC/ApsfSj0oyjRYBLQwdZid3n3v9QOiJroRC4oFS5z4Ltk4Xn3q8sjBjcaXz9hL6B78l46t3wrGlZDo+c6IviX6szqR75jjCbDJI5V6VN8/D7b1fEVKJhsR9Q7cTjutayxBkOCUsVdV9+9+lDAESPmlU0mFRkFTq2KoRDNmoY/LOJRaRGSTK8PXq0U+0A0sPG7GP/gFoj1zxWqqrXr1NhRvejiBEmALzxQXS0SlIuto2KIcJhA9zWIRULZXihqUH3rKgQEIbK4OahKcuV2xXEs7NWl77T994PFNvqERXNJIbSNpE8gw2Lb+dNXj8JnOGQsHKTWtZue4tV8FQiMbyRTFj44EdCiOTQkJDPkElSPfLwHKi4McKMoahq+tNzCgELKjOOLONbpi1Vy/vRrZKnQtBiaxtFxVyV6/bkuytD3w8r0+sRlhXeu6OdaCkPcYYki2w8qHvheVONCKtknOGQoNpRqi4Cq4iW/UlBlddrl2pnFpZspBIO6vHnl0vlVmVrCGmgsrB7xSlq7reukjoTgYZb6ECDgg2GRSRsioQZ886tzcJJX5RUOvQ4yZdjFDdv55TBatIYccrfHl9MpFNxi4khxo19CaNhE9i4umWyPuIzRxTK9h4/oFP9N30M7BIuMT88A2UBHZzhkKC6SKeqtqoik6wSFuu1Kyz0TnVy0EnopKLOV1VS3XMBJcHWHdEdXuNGhetyx8WHhe5XVxyJNmpYuo4qtQ/OPaSHdLs/ozqMTiHrV2FGJYywX88bBXvEDb9WDfu8NUCT6bMKsiUtiqHS1405MHLfx6at2PdaZYbiYZILJT4mMol4z9V6+4To0tFH9glfyPfuqTQLR+ngDIcE1Z9qoGKZSrGDskGQNLMfVcmOqHtT7CvC+o24IniqbQji7GKp1NMPCl7bAfSK/SyJMXL2ENWCwy715ZKABZVBjE7n8sp8NcHApLSP+nSMduN84ehCmLIYym67PbLDtWoWbngeeae0JnOMRKjSMxxBApVi+PGgbuEzDk/lQDVXJm2c4ZCwQTGmv0+ED9XTgbJVdN5b43h4avCi4ozl+lUGP45QjRVHmV86JriWREOdhRNNVK6gFzQQFjygyz2CdpNp17VKiBYK6wBlmc8q/aXO6PmBt9RCuUcYKtmaIl6X/p3qKzGMf291vW1RnDQweCAjW8COGkhEFUuKesxFlYTzDlUTMkxbLl2VTAwHEV1IRLOIaC8RDQ/Z73vF/WYS0X97CjumAAAgAElEQVSIKJUScVc/+l70Tj78onkAcMt5BwOwt8Cq4hr7xxuLtY/71xBRNgBYtK4Uk18XMmI8QJBe+WhrtPHVTSzMGtPfUfxcWOSYzI2lIgXvKdqOGhw+0wLUlV+/HDJAiEPQuttioVP2BkjiwGytwb0iqwboYeIBElsuM+hRA0QxakvVIISF+stoEzFrsoVSq4hoABHdTUTPEdGL3r8Y3zsTwHkAXgn5zh4A/g/AcGYeAqAhgItifKcyJnUbZDepF7Vja8Zx2fHRMfkmo+KoRX7VNQ5x8VjFKGzWUaSVXMLlG1Iul2lhmHVViCqubKZ096vqqqe99stfiLWfTxcXkP2IP6/nMhJl2OMmuPkHXUaZ1kIjZbO8qKdcTISMkrzxkhFVug6xPo9JuWATVB+FhwC8A+A6AD8U/hnBzLOZeW70nmgEoDkRNQLQAkB4/F6GXH5S/3rbDu1ViIwQbxjVHBEZxx8QHa1h8nzpSISEIYYJqxhLHdeWrCqiX+tI5JrR9mXLBymuafkRL0Xb5uoL6UC5Xz0KWyqvSdI8oMMUE0FlIb9xcyNenFMeeXZXgJpAGOJs6aBu+oEMOqdQV8xHEitYBiHWVA/K0reN6pO7m5n/zMxvMfNU71+SDWPmFQB+DWApgFUANjHzc0H7E9FlRDSFiKasXRuvYpgJshFEW0mex8495p20klEweMDEYkdRhEUFlRVzUjjWc0LylixEVqR54/ozurAzDVqo/8lZ0ZE6QXS3kDSpu8i7SiOb2manEdbMOMmjQeOJP74YHolk0tGH4Y3S99OIiPt4R6kTlz3bURFnQdI3Ml6ZXyhre+NTsyL3jUpMTALVb/kfEX2LiLoR0X7ev7APENGE4tqE/99YlS8kovYAxgLoA6A7gJZEdEnQ/sx8FzMPZ+bhnTpFj8xNCXrw2ykWRIoz41ZZBF2+IdmSo2EPh/je7NXRUh1ifYzBEVEmum6i/p3lx0syZDgI0c0St352GEGzPJ0wVY+we+1bJ5VCUU3LBPuJ8uWvU6z9EYT/yujmRAF6AywZi9erR+h5hm3DVr0CY4O6hj9HtlB9HC9FwTX1BoCpxX9Twj7AzKOYeYjk3xOK3zkKwCJmXsvMuwA8CqC+ElzKBD33qqO9OFNulY+uV1iUToM/K1TDE8OUo0ZrJvXIZRzVJ319oD8KKrJRobNhEv1RNA64By/wlSJV8e+r3qaqoeEeQe60uOtHUXkRfqI8qSsNar5HGWidJ1/nfn/g7ZJBC1PqtonSz8XMfST/9NTT9FkKYAQRtaBCr3IKgNkJf2ckSY4Yo7D91UnqVanY0XeWqIcOv7/Kzsg2qghVEohy4VGS20fEUHi9NCDR1C90uHVndGffMSSLvX2LkntHdyDkHyB4RaheXxCvgl3U7XaHbyDjdcxB7VdN7hVp0qgBerRrjvMPCwi11bhUZxUDTU4aFO09eX3Bun2v06rLEWo4iOjk4v/nyf6ZfikRnUtEywGMBDCOiJ4tbu9OROMBgJknA3gYhUX594ptvcv0O20x70MFtdQQVMJUg9B5SFWkQWwnJoqoLI6LtZ6jIsauebR+VcGoy/HdqBK8AIb0MB/hKyNciqh60p87qn5mdVBCmZ+g8FO/G0hFbiNsUCHW14j6DaIyxeNIoIhu46j77V2fZE2UgKRpxvaKjZ9g8iK5Edy+W/1chxWLSh0Q4HIVEWcnaeV9RM04Tij+f7bk31mmX8rMjzFzT2ZuysxdmPn04vaVzDxa2O8GZh5UdHF9npntl6vT5LX566J3AvD1gI7wlvHhVdhk0VkefTX88ypKs0ERLjZQzRfw6BUhDCc7HXGx8dD96+tFfeXYZHIRdBHbHhWG2Uky0v/mv9XiUII6jda+2P47Xlog31ERcVT74JTwhMKocq47YmhGiX17mBK0jP8VBRaDzEMcl3LQOuOM5YVnQqXSoPf83vBk9OL4JsEI5mJxnJlvKP7/Jcm/L6fSwpwRluMhJsB9L0DSOSoeXVaXwaNne3XVTZWRR5wIo7Tw5L1lI8pJwshOVovB73OWXVtxtJaULpCOv7pdi/pRPl6HE0WQLLjfWG3SyZ+JQFQdNuFXvnK2Ou5T8fc6VSH5UcatxSRdPyr12U1pp6CqrRNaLZYYtlkSOQxl80REY4joR0R0vfcvyYblFdmI0OPuL5SS4IN+wMhwTEtTTZXoCjEUMSltIlWCnpPTPJdbxHWRGRa/v/e6MfWLPIkjSxO/tp8NEldkau4DxS96eqa+fEdSLPJpgal0qh6i68f0GgfJjNi4F4JQMQq2K2PaRjVz/E4AnwHwHRQe4QsBhBdMrlLC1gXqOrbEaz8+CfN/fmbgPinZDVx8VPTPI3Y0Qe3aoeGXjUPn1uF5HFGdYtDb3xtVmvkdun/9GYeKTIcOby2uX8M9NcOh2NkE/dZJDB6iiiP5R/Y6s75/GsjrqLJds1yyDrL70E9aLidTVFt3NDN/AcAGZv4ZCovacu2ACkZlQTlqMa9n+xb71Fdl7NapZB8DlS5E7GeCHldRznyYguz3KYoKuX6DFDTC8oIJZO4VsZ8LqpVxxagD8NKVJ2LaT06Vvq9S1lcHWef7nqKrKYyg+26VYtExFZJw1Q2JuGf84bz+ksa3BLiSAGCrcE3CdKnCCDpjHakXXS48PFrgsEEDQpc2TcsCEfKEquHwnGjbiKg7gF0oJOZVFQO61Ffl9HPzuEJE8M2fGqJ1bM919MHa8CSgfhJl0KToK3zXso/kUihl3bnC0FlVyl23k5LVYRc76bAZS5+OLZVqZtgYcMs8HJs1tc9kNlQskSqiIkkBRGfmA/GSU4MYqVBXe5NwDv06lweAhLVbvId0MsBFghbBxeRU26iGzLZs0qisFHSe0MkcbwfgNhTCYxcD+E9SjcoMDZ+Cbsd3QETFL4+o6CKbiKN8sSCOSFktDs1jhqEat+8dT1bm1XY/Z1Lcx4+NPB9ZEMR3/zs91jGH9YqeLdqqlb1bsJ4yF+NZAbNDXaYrltYNIw9lkYNYuG5r7PyWpIg0HETUAMALzLyRmR9BYW1jEDPX5OK4h+7U2LabO8ofratbFHQ0MSJIZdQalITm57mAEbQfrzOT2XQTFeMwbLhq9lhwRd449iDlfVXHOv4oP9n9Y0uuZvKi0jqPbCDhL6UsGuywEHH/6Fu2nqSLbfecSoi7zXXv9TErOpoSaTiYeS+A3wh/72BmO2m8NYTtIImokVJbRe0sj/kBiY1vLizlrXz31OiEum5t1UqmPBRSjEpGVBEdU8RM8sd9s67dBpE1NsI4dX471RGzf91NFhY+bsYq5e8NQ2yT7L7f6/vuce+Vvrd3h3JXlTgDf16hvK8ungqt6vYozhjSFftHeA1aS9R/Tdkes3a6KaququeI6HxKK589I8QiKFFaNbrCcWGXbruBrLnMrbBRsXKhjKAQzeeEh1U30sNGlE7SJZfFvAG/Eq1J2KqNEezgbvJsdtmx31YcdfdsXx52KvtpdNxsA0Ncr+Wlhuvf95eMKI/4C5Oe6S/kRplcWVltcJHOAbPoMIWHHwTkaAGF3yiqFo3qOqCYFxbEjGXx3XUmqPYE30ehJscOItpMRFuIKJ/FcGNwkSCPEHTjeIu0UXWt/YRZXH+tABVk/VMiCWwRnYAfse9ZqSgJHha7n2Z+ib/jNInlDyvrq0pQDpAsS/ve1xcrHbNpo/JjyhZdddxsZw8LXqeIukvqfCWXVWuOmNwLYWUAwvAHNIjffVCITM2T767E9l17rTyLQ3tGZ5g/8LZaKWDbqIoctmbmBszchJnbFP9OQeQnXcTEnKAffl3Rp6g7+Qqr1a2iHeTH1kKmbboKrqqgK+TvADqHJFWGJVzaxu9C0WFE34I44dQlG2w1px4mJY2DkNW30LGTofd/xKNheu8uDYj8C0M3kS5oJiVmZ6s03x92b2L0PIMaNoN5eV6p9pB3D6aBagLgCyrbHMGElfU0eZCysBsqNUdEX3pQ37LT10OFJfdFuRps4u84xWb59Z78nDLIbiJh0sgGRjr3Yf8QN0qUxIrpvfvHF/U1tnSDRK4KqBz5wuySV0Cl/f7Jm2rYtIwdCSYjmhL6NBBRMxRKtnYsFlbyfoU2KBRXqirEjsKoJnHYsUMeJpNp7ZYdu1KRCJ+zqrRorpILIRJ0zv6COJefHCzs2NhGoW9FFqz9OPC96yN0vVQ6qKEKCZS6mI4fZNE4OjOusIz7J6bLQ7s9dAM34iBmrm/dsTsyGjLoftNd3bVZfkH1u5NeDxSJeiq/jkLRpkEoL+D0BIA/Jdu09BE7up/9733bBw/E5B57Ylp9cTmTiJ5mjcNvgfdXmS9l/fPNxdLtd75cXhshTDo6zXCMV+aVlxwWAwPC1AAAQEWhXlVLKgy/28Kv9RRGd8GN+LhEnPC5YtJb1D0BhEcJpu13DxNGFM9lusJCctBPJD6jh4UIkXrYWOPwBiu7A55rf9SfSRSgKVHquLczcx8APwdwSPH13wEsBPBmCu1LFfFhsJ6xGXIfmbiq/O4eAPjLK9FV9/zY6MyCCCqVuXZL+Wg3ybogcRCjqqKSA1V86Tq/c5As/4I1wbOiKPZrFT5j9NYQVHSaxDWOTT43TNqxl2GTUpmkTNh6Q9BbL8wuDSJUstTjrJd5eOt7n0iiLv/++iIceMOzZduuPG1g7O9URdUPcAEzbyaiYwGcCuBeAH9OrFUZ0UgYVSYlsS3DlryBSbnLJA2Hqpx4347BdUZslYyNS1Sf31DBpaZViCtAo+gX482LYF43JhkZff8AIUrYMAhTRdiw75PNFMN+hqABwktz10q3B+F3VZn0Jl5lv9snzC/b/uCUZfjZ/96vF1TTVTGHygaqhsMzeWMA3FmsG24mDlOjUMiVfsEgHFeGyUz1q8clJzmmWvchLELHhgyIDaI6NRWpbJ2k8iAF1Zkr5K5DFbFG3dwjVfwG0XQwMqSH2RrQ9xVH2l4zw9Yf4g6kvDK9/muy2aAGyoZiXtYHvrW3Hz08Q7p/ntY4PFYQ0V8AfBrAeCJqqvHZimTNFrup/G2EbNFtCjWfo5BNt01C/o7t33Hfa9s+0tcW1K+WqFtEKC9Rxy2ahHe6tl1Vuqh4+5JalPaflcyNqsIPDV0t/RWFQb3Kh0+GFJ8aoSDKGEbv/QqzZ/8g4Q8vzJfsHU675oWxuRgEE/aMq6xN2UL1mz4N4FkAZzDzRgD7AfhhYq2qcvxS0ibI7h9v08EaIzdxtG8jeS2KxxSTvTxyYjcwanC4XPzBChFTKouqpqhEdakKaLbWnJnY8OcD6hnVALBYCApQnSS8ubAgGBjm0o1bQMnzjPlnNTsMjOlXit4AsQ79xHnBLjOdCqFxUU0A3MbMjzLz/OLfq5j5uWSblj+WJqSXZMIKyc0fJggYhOi+kC3C2eanmtFqKrOoNGqLRyV89glYpxFlYL6kKADpESRtIavNYXOt6lcXDNXa/2+vL9L+DpkhVtU5A4BX55c60DwKIdVL6i3exn07Ba/n+WlaNKQ/fqSU+JnG4E6FqnY32Wb9VkvuKwsDNFnI48TiAp5OVvtAocSsTmhnGDIJdFNUBrN56DeCFmijasyH8fUT5MmPsgxqmyKaHTWz9d/4QF/6W3ZddEoKiB/v0FKvvUnOYu+fXMhRutdnTL21Oh0DL9vXlhBlXJzh0MBaAfuEezrTw//zzSWB74XJgvhRkV/3iMrIDlocX7Je31URhGkRIJGgNoibdW1IkOtGFub8jQAjY4Lu5dxj8FwMU9Bh8jhxYKd620RhUF1ZmiQjJrcU3dDrfVp33pqHjoHXzXpPk0wMBxHdRkRziGgGET1WLBIl2+8MIppLRAuI6Kq02+kn6QQblYdfRaHW1iKoqe96h4b21jkR0UBBo8l1H5cezDw8YEGzPHG7SYSYLFrqd8/PA1C+Vtauhb0gR10dtqDdB3UNTuy8QKF8qsf5h5X2XbCmoGRwy9NzlD/v53aDhWpVvJmn3zh52lVRiaRln0kxoU+XrGYczwMYwsxDAcwDcLV/ByJqCOAOAGcCOBDAZ4komUB0RR4t1mu46Ih45daDMkEHd4uuEvjrTw+L3OdHZ9hJBBLdCTpTbJ21kqgOJGixUmyOad5AGpTNOAz6gV+eX7/m9pzVhc5zgzCqNVnUDRp5qx5q7CEFo9YooDMMk1hpY1iTYmFE6eWs8X4H/7WdvqxQwijoWsmIquuRJZkYDmZ+jpm94dIkALLe40gAC5h5ITPvBPAAgLFptVHG+GLBGd3qf378khseKp1zq6bR6wdRoaMm6MxixAp24qxFttAdJhcRhnilPivI4ZuQZJis+JOazDhUf0uVPBI/D0+VS4OoDhI80cugEgRhl7VtiJS+n/JrmG88w+G/pzxV7StPC67l4SfP5Y/ysMbxZQBPS7b3ACDe2cuL26QQ0WVENIWIpqxdq5flqYrngmmqETYow18wyEPlPkkzk1q8+a8/W32yVydUcRNj+mWLqEGFdHSIK71uK5xUhvh7tTd0J301IGpMnBGqzjjEe8zvh5ftE0aUi9DWVRXXoP5uEMGVJiOLeSD9AnJLesUImfVL9YjorEHaIDHDQUQTiGim5N9YYZ9rAewGcJ/sEJJtgfciM9/FzMOZeXinTvUX00zwa/B408+4M46gEFMlo5DRIEQn61gsgCQanwmz7Zf+BOKHon7zxGB13rhs/KTUOXc3nF1dPXqwdLuX0AboV2cMY1BXtVI7Ua4UnV/l/04JLkt8dL9SkuqkhfHrjKvihfxOWqgeNfbpohs7KK8nzr06c2Vwxe5PD4/nPtclMcPBzKOYeYjk3xMAQESXAjgLwOdY3pMuByBejZ4AglM+E2BdQPitbvU/P0G+ZbUZRzS2quaJ0Ts6N7wYwz5bUNf9u2K1uiDEjHtxGh93bfyoBAvg/H5C/IVY2WyCmfGhUFxINUDga8eVxBODbhPVRLwxBwdXAdTlhAEdo3eyTNQt7XkGxOt8ZoCGmEeDgMXxuGzevgv/Col6PPXAdOvBZBVVdQaAHwM4h5mDsureBnAAEfUhoiYALgLwZNJtO0kI/QvqgHu0i7doFXRLHXdA9MOj4ve0FVX18JRSspHOQEn0yz/9nn7d7iBmrSwZIW+9CYjvCxZ/ZtulapMqfbt683YjpVwxtHVJgHqxKrLrvmN3KTDCXyLWz0PfGLnv9eG906leN+/DUn2Z31wYHmgi++2CxCc9gtY44rJr997QEtNBdeqTIqs1jj8BaA3geSKaTkR3AgARdSei8QBQXDy/HAWpk9kAHmTmWUk3TCxWFCQvHVeWIGgw0loh0qSxwnd3MKyz7Ec8z7CKb37EaXqYoNzPzx1i3J5HhAxam9G4aQrFxWHOqi2Ba2WqPDglXhayzF6Lxv3iiKCFI+r2w9hDuuPvXzwiVjt0ELPuo9acTO4FL8Jv/ccBAQOGKz9RMxgduRYbZBVV1Z+ZezHzIcV/3yhuX8nMo4X9xjPzAGbux8w/T7udv3xGHituEsEi4i8YpENcEbYololZycJpNmtslg0e5p46/aDw0Zsf0V0mdlrxZxylh9Kr85zUTMGUL4zsXfb3QwERUVHYlCaRHYsi3vdz+0WH4qRB4TpgfkzKB3iI7teoTtwrd/DustLaQtRt4Z3yzePk8vemHXySuScm5CGqKrds2CYfNZgmnB3SSz1bNogkkt16ti8t2r4pLATOCpDwtkVHzZmReOY2QxXFvmB1cRT/poGMRpIc1ad8wDDe0AV4RJ09l1DzqMFEQoEcYv2aLm307qGbnipppUXl1XiuIRMtriC6tTULkLhvcnm5ZTEx9PFvHxOrTSY4wxGCTEwuDo0TrHQXZ4Qs1tMWWyiTRc+SslmGxeM2ayTKVhf+3xrjt68ncGcBmeyGCUFuVpP7R1bzngJmhTa54cmSx/rGsdHuzguFJFNRccBkHeKUCJXktPjDRYdg8a1jsPjWMVYGpLo4w+HjCyPr9r3+wHKWapIJPToyH36aCiPHu15ZaKM59ZjlCyUc2CU6S96P6C6zeSlVJNF1mLKkFDIadx3CI2kftq0qlHNi1Kg3QUUCPmqRXuv7DDPeTQnUQMs4OdAZDh91HeQRU8skiqS65EBSSYrYrvkxalqHsdrXgZpUHhRzFZJOhLR19MmL7OQd6GgcmbAuYDFXlc3bCzlPvynqaKWFSqBKUB87ol+y64U2OPfQwJznTHGGw0fQ7DVovUMHWWfnH4mbEif878AUQvlufKq8DkfcuPPVm+2M5P1sKXaAplXsKpWw6DcVPN0sUTfMZlJiEH0Vqv8FaZkF6WWdd5h5Z+2tF9pyLcoS+w7bP33XlB9nOHwEPT42o1FENm7Tr0UsI07Cka3w3TCW+Ipgmai52nL7hHHPa4WF0OufmGl8jDRlYQDgAI1Q6SDiyq545yxOAEwj8XRQkZvRDZ9XyacKgojQrkVjawKFsgHh7z9zqJVjx8EZDkVsRNmcKxnJxJkpiA97nvMPbMlD//iRGVaOo4Louvn6CX1D9sweG4Wz4mY6e+Oqzdvjl0W2zUWaIpiNGsTrFhsSlV3POIErsrol+we409PEGQ4ffgFD70f/56TFsY89ok99n2qc51U0OmLGbt6QVSs0wTR5Ki7dNUMo025nXDcTALwcI7cIKIWJizVC8oKOzhoQP/qxQQMqezbjuD3j6uIlhTMcPvw/lKdI6cWsm8qAA/JFujguAjHn4t6YOlBJYkOvCUjOXWidhOyGrE43YFbnw09cw/HMTHvSMjJu0FBnjkvc+2ztlh2YtnSjpdbkE2c4IvBGc/M+LEQbDelhdyF5V4zRiFgHYdMndtZKZJyhmeHtx6tFEJdKMRxeoSXbHNlHnryXB1faCwkpH3tEJhtapG+nUvjuqk1mWeriPZAzEQIrOMMRwVLfoq7tm+CV+eYjvT1laxyF13FrUwDAG77Ev0uPrtM+xuG928duh59F6/Jd/c3DH0Fmi7GHyKN9BhjkxHjYCDMH4uUR5Y3+nUvXc+Lc+LV9bPYZXz8++0EC4AxHJP6EONthmv+etDR6pwBEw+G9tlFGddqy8mm2ySEvC7nBvzsquPZCHrEh89KxVfya4EGt0F3Y/uyRpRDPd5ZuiNGiEvM/TGaW5TFUskicBjaCYuIq5Z4jyIt866Tkasfo4AxHBP762UN7mGcZtxPKZdqQM9ktiaqykWR427NzMU3oUGy7iLpqVv07tn8hPFImrZCGC6OvYuaxV6elpSTKycaos02AXL7usU8eVMqhidupeZnbOrW0TTiwe30X8ZEWdbeCePLd+CWA4l7jW84r1Z23VTIhLs5wROAvd6ob2idiW65gu2DUlm8ouBxsiSAuFORWhhu4ncKelQsOl5WYD8azWzL7ZSvRKoxj+qvF9fculsyVTQBs1GcIyovotZ95wMbLMV0xvYr5CkF1x5Mk7eJFpm6iLTFDlFs2bbRPlyovOMOhia3Btz9k0y+brcJDQj0Fr6SmaTy+v6rhC3NKi50mxmhwt2C/u+nodPqyfEeqePeGLDw2yTwb3WRK8dd8fHr5iHqopm6XeL+lHYqbdtizav7EiL77lUkXJaX/liXOcCggjux377Fzs/o7eBO55fclgnI3KaiFyjjtwPLIKVPZbg9v9G0T2aA9TxEr3vqSzHjbrggXh0EhRv1bJ/bTOpa45rcqRp0ME84cYq90rU3at2hSJkrp3Q/tW+TDzWQDZzgUWCE8ELbE5vx9iy3J9c6a9Qk8bNdIrkU8aQvZtcxTKHHP9mEjZ712isl1aZ9iL0uyHqr4B1dBNGxAZeuPHlkr2trEGQ4Jfp/+ms2lPISubfUWdoMYN2NV2d9Jq59GcUCX+HpH1YJpbo2/YxCz+X95/sH+3Y0YZlkCPi4/EWq5pKElliWqoe5Tl2woWyPMSvEgSZzhkNC0cfll+ezdk6x/xwKffHmjBIs8qXDo/vbzLmwR5uVJYhD3lqEUethSUGfNSLIgbFbws4E44xBLLZ+iWQ42bb5mIOuvSpABrZ75hjMcUtJwR/tHtWcN7R6wp8M/YhOT1n5w2kDr3/dSsWSoLmHuKFv31GePMo/qU6Gdph9e/G227yrd03nzypx/WHkkX38LisJReDp3W3cUZp5JlH3OCmc4JBwcI1dDFb/haNNMXczs2tGDA99rbyBXniZi8pkq/oCErTtL0Tu6HZ0KD04xE2X013EoNxZ2LEcXSzOXIEb0NS9u5J9F2+Z7owYYf7abz8WcRrkVb63rsWkrAMTTucsbznBI+NxR+qGxuqz3xb3rLJyFrbOkvWCoyznD9Ivk+GsbiPUubI3kf35uKRrNVBo8zAduq51+pdcOLeMPFOJoiQWdVxKz9i8fW7fvte6a0ek+vbWubZOvQeMPy77qzEGJf2daZGI4iOg2IppDRDOI6DEiqpcSTES9iOglIppNRLOI6Iq02pfGYlacGs95cwPoMKKvvo/+8pPLZRbE87f1W+lKp8vwu6pEdYCk7qhfXzgs9jEmLTSX1egXUIHvM0fozyyjEBNoZZXxwjjIl3kuZs/L+GyMRF8vJ8ofXZd1AIxNsjqT5wEMYeahAOYBuFqyz24AP2DmwQBGAPg2EaWnrZxj0qww9/1Tzd0D142p71IzCUn0d8hlf+UoYMWvE/aXFBK/DrYQZeWvB69D84AiUjbaJcPLoNa9j3RvOxO1BA8vgGGXz8Uap6BT3sjEcDDzc8zs+QMmAainQcHMq5j5neLrLQBmA0ilcns1+SLj0jtGtbGDuifTeZTPOPKDv3PaKSjG2hxtPnDZiH2vO1oo+ztzhZ269yJ56yN1Dc3AruaKw95v7Z9xVFMeRx7KS30ZwH/DdiCiOgCHApicQnsSFWzr07HlPnnwTYb1xtO8/+J0eCP7mS+0huFFqQD56qD8HYP4p838ixF9O+DmTw3BCQPs6HT5ZUccwBBfgIxOFJYXPOUvmdzIRQahklcAABEMSURBVFVFQ0QTiGim5N9YYZ9rUXBJ3RdynFYAHgHwXWaur7FR2u8yIppCRFPWro2voS9DLPBiinjrLPnIrL7E4G7l/lpbU+Cxh9QPCfYvKsbBNATSf3q3PD173+sOFuTKASQSZC8e0vZo85IRvWMFQiQRjVbN6NSk+c9bhai8x6evKNve0BmOaJh5FDMPkfx7AgCI6FIAZwH4HAf0fETUGAWjcR8zPxrxfXcx83BmHt6pU0KKqTb6Zwv3Th+fzLffl2qKTF/K5s1+/9eOMvqcfwFcVBu15QLqFSrDYUaePRNhId02yKNh6tne3AXdSWOA4lUNXL6hXLtrUAz3V97IKqrqDAA/BnAOM0tLkFFhiPZXALOZ+bdpti8I29ndtuShZq6046P+yjHJZdMCQMeWZv54/7AiiQ7ZdkLY7j178VyMyLmkSVqpoEWTPHjBy5l45Ym4/KT+mH3jGdqf1Zl5N2xQ6Fb9A7qka5akSVZn8icArQE8T0TTiehOACCi7kQ0vrjPMQA+D+Dk4j7TiWh0Ru0FAFx/1kGxj3GYIO3xPwtFYgDg9fnrondSoLVGEqIqH/xiNI4f0AkTrzzROHPWb1/zJBgYxKyVm7FkvZ2yrEmQZmReXmjUsAGuPH1gYCRYGDquxn5Fl3YeZ122yCqqqj8z92LmQ4r/vlHcvpKZRxdfv8bMxMxDhf3Ghx85WfazkGz1lWNLo/rHp60I2VON5Ru2GYvy+UmiP27YgPDPLx+JOsUqejLErPpN23ZVRHRK3ps4KuUiSJXI1OtGGX3uytMLMjhH1OVX/y0u+ZtP5oS6Di2w2DdibGDBzIodikx6WZcl67dhp6U1jrx2yB2EkNMVGz/Bui3mmc66XDIiWW2orPBnoDvq06FVU6Oqey2KM5qdu3MU8meZ6nG6Webw3vUznAd0tru4tekTs3BcEWZgfVEyQlbrutpo2IDK6qMkTadWyWpDVQNNG7luRMS7HrY8AXnE/eIBiLo4HjbULWW+5XOGmSvjMhgPTS2UkN0qSFyY0loYiV6dQ22dSolofGfJhqybkBrVO642w4v0ezfnpY7j4AxHAEmpzMqE8FrGcBvEkYuQ8ZOzS6ounzo0lUR9LdKWpjYNhElDbsQ2piJ8fWOsX1UjO4qKAfe8tqiqZEZEnOEIoHtCsiOyBfY4UXo/fHjGvtdjDo5fg/kCoW5B0hLeJjS2sdCkQUPD76uQiVEZTQxvxGslmmS1jHgdq9RuuMXxPDC0Rz1xYCM+PzK+HHyDBmS0IJgWDVOulHjSILNk0rwGGoThr3ypit/gDKjxMsSimsHeKrUcbsaRA0407Jz8VF5Xpc9fX12U6vcdYBgQkeYCvi2O7tfR6HP+rrFRyrPCvCHKv89ZvSXDliRHbf/CGrRIMGKpTTM7iUKVOMrVZerS2ll0TpvehtpX/pocB/pqX9QqfTu1NC5DnHec4QhBFP27ZERyVQFt6S3VQmy+6Klq3jj58ONKieKygem4wx/wcdnxfS20prKp69ACQxIqK5AHnOEI4fKTSpXnvnFCv8S+x5aQYC2M9ET9H39VtySohVmch61zNV1kryYWr9+GJy1JCuUR9wuHcECX1hg1uDN+ce7BVuRGHPFZs6UUflydy47pIavQaIM48jLVxtwP3RpHTXLPpUfg4qOqU3aiEvlwc0luxHaM/JAe8WcwY4bGD4lOi2YpuPpqnadmrMq6CYngDEcG2HBN/eQsV37d9ozja8fVlm/+gsPrVWx2OJRwhiMDbGj7tK6BhfAobIfIJ7We0dFWlULL2JxxTLzyRADA9W5AI+XkQZ2zboJVXO+TAece2gP3TV4KAOhqmp1dO2u2gQy1WMcbsHNJZe6zmz81xMKRk+VbJ8YL/qjr2DLXiaNZo1N6thJwM44MOO+wkgZU2+ZmORwDulRPGUpTbD+MNq7pHolUftMcryUM712oGXHFqAMybkl1U22aVW7GkQFiB3WNYWTLIb3syJRUCl3bNMPqzeWCjrafxYEWakJXQnVCkQe/PhKf7NqDpo3ya9yqgeoyG27GkQmiJMFAN3NQQlbuM4+juB+cNrD+xvw1cx8NGlAsdWaHGjt2VVdtDnfHZMTXj++Lj7buRNe2dhRou1k6Tl6RicXlsT9uL6kzzblsqSNNencwk3PJK85wZMTVo+0mX91wdnVHs8gmF0k7hb5osIayR9LQpGq7OPLLiQM7YeLctfv+HtytulQdnKuqShjRt0PWTUidpF0sJjLjMgN36P7tLbTGUUnYEi7NK85wVAnV7qeWSb4kVWzLY0QffWMsi6py1B5JqmnnAWc4qgRbCrt5JSldpTCOPUC/PoUzHA6g+lxTfjLpbYjoNiKaQ0QziOgxIgqMLSWihkQ0jYieSrONlcBPq3xdQyQLXSWTNZSkZ0GOyuD8KpdzyWqY+jyAIcw8FMA8AFeH7HsFgNmptKrCuPToOhzdrwOevPyYrJtSNYgL4o0MZnG2JPIdlY0NWaE8k8nZMfNzzLy7+OckAFLzTEQ9AYwBcE9abaskiAj3f20EhvasrWTAJPmaK0LksECjKh9A5GFF9csA/hvw3u8B/AhAZJYcEV0G4DIA2H9/J4NebaSV69ejXXNMv/7Uqg82cCRLtRcAS2zGQUQTiGim5N9YYZ9rAewGcJ/k82cBWMPMU1W+j5nvYubhzDy8U6dO1s7DkQ+6t0svwbFdiyZVH2zgSI+e7atv3SuxYRUzjwp7n4guBXAWgFNYrh1xDIBziGg0gGYA2hDRv5n5EvutdeSdDq2aRu/kcOSIz4/ojX9NWoL7vzoi66ZYJ6uoqjMA/BjAOcy8TbYPM1/NzD2ZuQ7ARQBedEbD4XBUCjd9aggW3zoG+1eZ3AiQXVTVn1BYt3ieiKYT0Z0AQETdiWh8Rm1yOBwOhwKZrAAyc/+A7SsBjJZsnwhgYrKtcjgcDocKbgXQUZG0zLGkgxjDf/FRLsLPUX04w+GoSPJcMGn//Uo+7VMHd8mwJQ5HMjjD4ahI8rzgKIbymijsOhx5x93VjorkWsv1TGzyk7NKGmIja1Du3lH9OMPhqEjyPJLvtV8p4avaM4gdtUl+nz6Hw4co/9O2eX6r6vVsX3CjDevlNMQc1YkT5HFUDCcM6ISXiuU4+3dulXFrwll865ism+BwJIabcTgqhm+fJE3/cTgcKeNmHI6K4fDe7dG/cyv88PSBWTfF4ahpnOFwVAxEhAnfPyHrZjgcNY9zVTkcDodDC2c4HA6Hw6GFMxwOh8Ph0MIZDofD4XBo4QyHw+FwOLRwhsPhcDgcWjjD4XA4HA4tnOFwOBwOhxbEzFm3wTpEtBbAEsOPdwSwzmJzKgF3ztVPrZ0v4M5Zl97M3Ellx6o0HHEgoinMPDzrdqSJO+fqp9bOF3DnnCTOVeVwOBwOLZzhcDgcDocWznDU566sG5AB7pyrn1o7X8Cdc2K4NQ6Hw+FwaOFmHA6Hw+HQwhkOh8PhcGhRs4aDiM4gorlEtICIrpK835SI/lt8fzIR1aXfSnsonO/3ieh9IppBRC8QUe8s2mmTqHMW9ruAiJiIKj50U+WciejTxd96FhHdn3YbbaNwb+9PRC8R0bTi/T06i3bagoj+RkRriGhmwPtERH8oXo8ZRHSY9UYwc839A9AQwAcA+gJoAuBdAAf69vkWgDuLry8C8N+s253w+Z4EoEXx9Tcr+XxVz7m4X2sArwCYBGB41u1O4Xc+AMA0AO2Lf3fOut0pnPNdAL5ZfH0ggMVZtzvmOR8P4DAAMwPeHw3gaQAEYASAybbbUKszjiMBLGDmhcy8E8ADAMb69hkL4B/F1w8DOIWIKMU22iTyfJn5JWbeVvxzEoCeKbfRNiq/MQDcBOBXALan2biEUDnnrwG4g5k3AAAzr0m5jbZROWcG0Kb4ui2AlSm2zzrM/AqAj0J2GQvgn1xgEoB2RNTNZhtq1XD0ALBM+Ht5cZt0H2beDWATgA6ptM4+Kucr8hUURiyVTOQ5E9GhAHox81NpNixBVH7nAQAGENHrRDSJiM5IrXXJoHLOPwVwCREtBzAewHfSaVpm6D7v2jSyebAKQjZz8Mclq+xTKSifCxFdAmA4gBMSbVHyhJ4zETUA8DsAX0yrQSmg8js3QsFddSIKs8pXiWgIM29MuG1JoXLOnwVwLzP/hohGAvhX8Zz3Jt+8TEi876rVGcdyAL2Ev3ui/vR13z5E1AiFKW7Y9DDPqJwviGgUgGsBnMPMO1JqW1JEnXNrAEMATCSixSj4gp+s8AVy1fv6CWbexcyLAMxFwZBUKirn/BUADwIAM78JoBkKYoDVitLzHodaNRxvAziAiPoQURMUFr+f9O3zJIBLi68vAPAiF1eeKpDI8y26bf6CgtGodL83EHHOzLyJmTsycx0z16GwrnMOM0/JprlWULmvH0chEAJE1BEF19XCVFtpF5VzXgrgFAAgosEoGI61qbYyXZ4E8IVidNUIAJuYeZXNL6hJVxUz7yaiywE8i0JUxt+YeRYR3QhgCjM/CeCvKExpF6Aw07gouxbHQ/F8bwPQCsBDxRiApcx8TmaNjoniOVcViuf8LIDTiOh9AHsA/JCZ12fX6ngonvMPANxNRN9DwWXzxQoeBIKI/oOCq7Fjcd3mBgCNAYCZ70RhHWc0gAUAtgH4kvU2VPD1czgcDkcG1KqryuFwOByGOMPhcDgcDi2c4XA4HA6HFs5wOBwOh0MLZzgcDofDoYUzHI6ag4i6ENH9RLSQiKYS0ZtEdG7xvROJaFNRSXU2Ed1Q3N6CiO4joveIaCYRvUZErXzHnUxE04loKRGtLb6eTkR1RDSeiNolcC7diChQMoWImhDRK8UkVofDCu5mctQURaHKxwH8g5kvLm7rDUDMWXmVmc8iopYAphc75tMAfMjMBxc/MxDALvHYzHxU8b0voqC0e7nwdlJS3t8HcHfQm8y8k4heAPAZAPcl1AZHjeFmHI5a42QAO4uJUgAAZl7CzH/078jMWwFMBdAPQDcAK4T35urIshDRYiLqWJx9zCGie4ozl/uIaFRRdHA+ER1Z3L9lse7C28XZj0zZFwDOB/BM8TMHEdFbxVnODCLypEQeB/A51bY6HFE4w+GoNQ4C8I7KjkTUAQUNq1kA/gbgx0W31s1Cp2xCfwC3AxgKYBCAiwEcC+BKANcU97kWBZmbI1CQCLmtOAMS29cHwAbBgH0DwO3MfAgKQpXLi9tnAjgiRnsdjjKcq8pR0xDRHSh02juLnTQAHEdE0wDsBXArM88q7tsXBZfVKABvE9FIZp5t8LWLmPm94jFnAXiBmZmI3gNQV9znNADnENGVxb+bAdgfgPh93VCuufQmgGuJqCeAR5l5PgAw8x4i2klErZl5i0F7HY4ynOFw1BqzUHDvAACY+dtFsT9R3PBVZj7L/0Fm/hjAowAeJaK9KKxbmBgO0cW1V/h7L0rPJAE4n5nnhhznExQMite++4loMoAxAJ4loq8y84vFt5uiOopVOXKAc1U5ao0XATQjom8K21pEfYiIjiGi9sXXTVAoQbokmSYCKIj2fae4mO+pF/uZh9IMxZsRLWTmP6CgkDq0uL0DgLXMvEtyDIdDG2c4HDVFURX1UwBOIKJFRPQWCiWCfxzx0X4AXi66k6ahMEN5JMGm3oSC4ukMIppZ/LuM4uL9B0TUv7jpMwBmEtF0FNZO/lncfhIKiqkOhxWcOq7DUcEU808OZ+brQvZ5FMDVEW4vh0MZt8bhcFQwzPxY0RUlpehWe9wZDYdN3IzD4XA4HFq4NQ6Hw+FwaOEMh8PhcDi0cIbD4XA4HFo4w+FwOBwOLZzhcDgcDocW/x8RwTTMJES1vgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "strain = df_h5py['strain']['Strain'].value\n",
    "num_samples = 10000\n",
    "time = np.arange(0,1, 1/num_samples)\n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot(time, strain[:num_samples])\n",
    "plt.xlabel('GPS Time (s)')\n",
    "plt.ylabel('strain')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MATLAB File: \n",
    "   * Industry standard in engineering and science\n",
    "   * Data saved as .mat file\n",
    "   * we are goint to figure out how to load a MATLAB file using `scipy.io.loadmat()` \n",
    "   * scipy.io.savemat() to write .mat file\n",
    "   * type of the file will be the dictonary \n",
    "   * keys: Matlab variable names\n",
    "   * value: object assigned to variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'dict'>\n",
      "dict_keys(['__header__', '__version__', '__globals__', 'rfpCyt', 'rfpNuc', 'cfpNuc', 'cfpCyt', 'yfpNuc', 'yfpCyt', 'CYratioCyt'])\n"
     ]
    }
   ],
   "source": [
    "#Import the package scipy.io\n",
    "import scipy.io\n",
    "\n",
    "#load MATLAB file into df_mat\n",
    "df_mat = scipy.io.loadmat('data/ja_data2.mat')\n",
    "print(type(df_mat))\n",
    "#print the keys of MATLAB dictionary\n",
    "print(df_mat.keys())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  0.         844.75086505 874.15492958 ... 710.70382166 715.50154799\n",
      "  727.59872611]\n",
      " [  0.         643.34878049 641.84020619 ... 583.27021277 603.84897959\n",
      "  608.45679012]\n",
      " [  0.         487.2        486.08863198 ... 435.80705009 431.29063098\n",
      "  439.29222011]\n",
      " ...\n",
      " [  0.           0.           0.         ...   0.           0.\n",
      "    0.        ]\n",
      " [  0.         535.31235431 541.69194313 ...   0.           0.\n",
      "    0.        ]\n",
      " [  0.         793.31832298   0.         ... 584.53435115 577.9835443\n",
      "  599.96085859]]\n"
     ]
    }
   ],
   "source": [
    "print(df_mat['cfpNuc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(200, 137)\n"
     ]
    }
   ],
   "source": [
    "print(np.shape(df_mat['cfpNuc']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xb199c64a8>]"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WO380V0QPpXnikEXoLeumyGSsLz2idzuKXk+uiL7YktSpZZpASXNjtdZGRN8wEdFLYzNBKI76E/oSygXHYwnCsQQt/swRPRhVLqUI/VgknhKw1Bot66bIiD7dJvGZrZNjRUTPU3vRp9ZTQknq1DJN6zxQ3Gc/Eo4TS2ja/C4aJKIXhJKoO6Gf6CNTuNhki3TT8TrtZVg39kn3ldLUzIjoJ87jLSFBPNMRfSm5h1NBo4a+o8FNwG2cT2rpBaE46k7oS9mMZLU/yNSi2KLUnajBSCIlYBalDEgJRSYLvWUHFVN5MxyOYbcp/K7JFx5PCa2dM1o3JUT0p0YNoW/zuyc8eulgKQhFUXdCX8pmpFwNzSxKieij8STRRJLAlIgeii8DDUbjqc1SUFrJ50g4TqPHMW1TmNtRIY++hIusNXCkvcE14dFLRC8IRVF3Ql+K2FgtiptyCb2reKG3Ok5OTcZC8WWg4Wgi1f4AJrpYFvMtI5M4Q/HDWpJJzUiuZGwRFw2r/UGbX8orBaFU6ljoS4noK5uMHcsj9EW1KY7GJ1fdmBF9sAhRHBmfnkCF4iP6sWicpJ7u9VvWTVEe/VgUpaDV78Ju1uVLMlYQiqPuhL6UdgWDOaZLWZQS0VtCP7XqBixxLex8iaRmPJbMnIwtYk3ZIvpiPfrhUPakLhTp0Y9FaPUZIg/GZyURvSAUR90JfUkRfdhoqmU1L8uEz2UvKnqGylk36dOlLPwlWjfZIvpSqpQqUV7ZPxahLTDxTarB45A6ekEokroT+lIi+qGgsVkqU+dKi0aPk5EiLYWJiH76BaSYtgOhKdOljJ9LsG7C8UlDRybWUlwjuExDR6C0/MhAMEpr2v6FgMcp1o0gFEndCX0puzMHQ9GMk6XSafQ6icaTxfWniVhToTJZN4VH9KEp06Vgwrop1E7SOnMCFYydurGEJlFg18ghU+hb/Fki+iK+HfQHo7T53anbDW4Ho+NSXikIxVB3Qu8pISE4FM7eotjCsilGihChlHXjymTdFJ4Atbo5ep0T57HOaV1M8hExSz0bvRkuOs7iIvHUNK4pF8dSdvwOBKOTrBvx6AWheOpO6EuJ6IdC0fxCb27msWyLQsiZjC0iAZopovc4bShFwQPCc+3+9TiKK4scCpvzdad8ZsX28IklkgyFYlOsG4dYN4JQJHUn9CVF9KFYXuvGEsjhcOEilCsZW0xJoyX06R69UgpvEa2KU50rM3j07iK7aQ6HYrgdtlQ+ZOI8xV1krR3Jbf7JEb0kYwWhOOpO6EuJ6Mci8YyWRjqlWDdj0Tguhw2XY/r/hmJKGieSsZOF1edyFDxOMGdE7yyu985QlmlcLntxFtCAuSu2Nd2j9xjWTbHdRwWhnqk/oS8yok8kNaHo9H40U7Ei4WKsm2CGzpUWHoe96Ih+qtdvtE6uhHVjlUUW6NGHMyevbTaFy154NdHAmCX0kyN6rUub5iUI9UrdCb3dpnDaVcFik/LRPbkjeksgixP6xLTOlRZuZ+FNzazGZd5pEX0R1k2WFsXWWqD8iB6K2whm9bmZlIz1SBsEQSiWuhN6MPurFyhalqA0ZIm8Laxe6cXU0o9F4hkrbqC4ksZghmQsFCf02XazWmsBiBQo0MM5qpTcRbR26E/1uZkc0YM0NhOEYqhLofc4bQUnFq0Kj3wRvcdpx+2wpSyQQs+d1bopond7KJpAqQlBtvC5Ch8Qbl2gGjK8z4myyCIi+izJ62J22Q4EjT436UPZLYtMaukFoXDqUuiLi+gNQckmyOk0eZ3FWTfR6WMELVIDUgoQ11Akjtdpx2abvHPXW0xEH47hd9lx2qf/SriLjOgHc5SjFmNJ9QejtKT1uQGxbgShFOpT6IuI6EcLjOjB8LeLqrrJkYydmDJVQEQfmzwv1sJfpNBnm6DlKSKiH48liMSTWVs6G2MJC4/oW6eMb0y1KhbrRhAKpj6FfgY8ejA2TRVj3WQaI2hRTE+eUCQ+rbQSwOtyFFVHnykRC8VVKmXbFTtxriKSsWPZhV5q6QWhcOpS6D1F2AeFevRgWTfFbJhK5LBuCq/3D06ZF2thJGMLL6/MJvTF2EjZdsVOnKvw8sr+YGRSIhaQAeGCUAJ1KfTFJARztSmYSjHWjdaaYDSXdVN4FB2OZr5g+M0e+YVsLhoZz9y5EtInQxUT0VfGukkvrYSJXcTFWGSCUO/UpdAX03bX8uizlUGm0+hxFmzdhKIJtM7c/gCKq103pktltm60Luwc2TpXQvqGqQIieqtMM0cdfSEXjERSMxSOTdoVC+C022gPuOkZGs97DkEQDAoWeqWUXSn1olLq1+btZUqpZ5VSe5RSP1JKucz73ebtvebjS2dm6aVTbEQfcDumVbRkwqq6KSSCztXnBtLtksIi+kxCbw0zt+au5sKwbjKvxWlXKFXYt4vhlHWTxaN32okWcMEYDEXRmmnWDcDSNh8H+4N5zyEIgkExEf2HgB1ptz8PfFlrvQoYBG4y778JGNRarwS+bB43p3A7Co/oc9W6T6XR6yCpKai/TL4k70TVTWERfaZvHItafQAcGQzlfH48kWQsEs8a0Sul8BRoueS3bgpLxvZnaH9gsaTNz6H+3O9JEIQJChJ6pVQ3cD3wTfO2Aq4Gfmoe8j3g9ebPrzNvYz5+jco1mmkW8DiLi+izVcZMpZh+N7mGjhhrLDyiD0US09ofACy2hH4gtyha9lQ2oTfWU5hAD4VjuOy2jN8wrPMUcsHoD07fFWuxtM3HiZHxooexC0K9UmhE/1/APwDWX2gbMKS1tkofjgJd5s9dwBEA8/Fh8/g5g5EQLNCjj8QJZElSTmWiVXF+oR9LWTe5yysLuSCFsiRjFzR5sNsUh/MIfarPTY73WejEq6FQjKYcYxcLTcamOlcGMkT07X6AvO9LEASDvEKvlHot0Ku13pR+d4ZDdQGPpZ/3FqXURqXUxr6+voIWWymKmd40Nh4rqIYe0loVFyH0eTdM5bkgJZKacCyRcXC5w26jq9nL4YFwznPk6lxp4XXZU7ZM7nNFs9o2YOZHCrjIWkLfNiUZC0ZED4hPLwgFUkhEfxlwg1LqIHA3hmXzX0CzUspSqW7guPnzUWARgPl4EzAw9aRa669rrddrrdd3dHSU9SaKxetyMB5PFNQwLNfu1amkrJsCarwLTcbmi6KtmbDZvhksbvXlj+jN2v9sdfQAV6xq59FdvfQM575o5OpcCUZEX0iztlOmR9+S4VxLWo2I/pAIvSAURF6h11rfqrXu1lovBd4KPKK1fgfwKPAm87B3A780f77XvI35+CN6jk2JaPU50doYEZiPsfF4QZuloDTrJns/+sLG91kbojK1QAAjIXs0j9AXEtG/74rlJDV84w8Hcp5rKBSjKcc0rkLnzw4EIzT7nDgy9N5p8jlp8Tk5KAlZQSiIcuro/xH4iFJqL4YH/y3z/m8Bbeb9HwE+Xt4SK09rwLADrH7nuRgtJqL3Fj43Nl9E77DbsNtUXusmFLHGCGaP6PuD0ZxNwAoR+kWtPl533kLueu5wylbJRL75uoVewDL1uUnHqLyRiF4QCqEooddaP6a1fq35836t9cVa65Va6xu11hHz/nHz9krz8f0zsfBysCo5rBK+bGitGYvEM7buzUSgiF2bltD7MnjrFp4C5sZmmhebTiGVNxNDR3K/z/e/fAXhWILvPpk9qh8Kx3J79AW2U+gfi2asuLFY0ubj4CmJ6AWhEOpyZ6y1rT5XZAoTu1cLjegddhsBd2GNzcYiCfyu6a2F0ylkbuyEdZM9oofcFSrD4RhOu8qY0E1nVWcDr1rbybefPMj248PTHo/EE4SiiTwefaHWTf6Ivmc4XHD1lCDUM3Up9JaADARz7xgtdIxgOoU2NjM6V+Y+r7uAiD7bdCmLRa1eIE9EH47R6MleEpnOJ/90LY0eB+/61nPs7R2d9FjKAsqyKxbSetvni+iDUdoC0ytuLJa2+UhqODqYOzksCEKdCn2LKUSn8lg3qV70BUb0YHRXLMS6GcvR0MzC48xfux7Ok4xt8jpp8DjyRvS5/Pl0upq9/ODmS1BK8Y5vPssdTx/ksJkUHc6zKxYK67O/o2eEgWCUFR2BrMcsaZPKG0EolLoUeqfdRpPXmde6SbUpKCKib/QW1tgsGMlfzVPIfNVgnmSsUipvieVwOEZDgUIPsLwjwJ03X0LA7eCTv9zOlV94lM/dv5Mh833nsm6sHby5Zr5+76mDeJw2/uyCrqzHpGrp03z60fEYV3/xMe7b0pPxOfe8cJTe0fKboR3uD/GdHHkKQZhr1KXQg+HT5xX6VERfuAgWOk4wmGMwuEUhbQdCsdzJWMhfSz8ynr3PTTbOnN/Aw39/FY9+9CquP2cB33h8Py8cGgQmvjFlYu3CRuw2xdP7+jM+PhiM8vMXj/GG87uyNkYDw35rcDsmRfQ/ev4I+/uCGfMHJ0fG+ciP/8jPNh0r9C1m5Rebj/HpX70krZKFqqF+hd7vSvVTyUYx82ItGj3OnNHqxLmzDx2xKKTLZiiSOxkLhtAfHQyTzLJJKVeL4nwsa/fz6RvW4nHY+OrDe4DcZZrNPhcXLmnhoR0nMz7+o41HiMSTvHvD0pyvq5RiSbsvVUsfSyT59hNGlJ1JgA+cMi4IheydyId1IS9mPrAgzCZ1K/St/vwRvSXYxVk3joIj+kCeZmkepz218zXrecxkbK6KmUWtPqLxJL2jmS9sRjK28Pc4lfaAm1uuXJFaSy7rBuDaNfPYeWKUo2ZXzbufO8zbv/EMtz26l+8/fYiXLW9j9fzGvK97dlcTT+07xR9293Hflh6OD49jU2RMhluR/2AFhN76vShmbKQgzCZ1LPTuvHX0xUyXsmjyOhmNxPNu8R8roOpmQZOXo4OhnP3tw+bQkVxlmrlKLLXWRSVjs3HzFctoD7ix21Tez+vaNZ0APLyjl6FQlH+/bwfbjg3zhQd3cWwozHsuW1rQa378ujWsnNfAX35/E1/83S5WzQtw1sLGjBG9FfkX0q8nH9bvRTFjIwVhNqlboW8PuBgMRbPaGTDh0ecT5HSsfjejOfxbayNWPkE8ozPAYChGX47BIdnmxaZjCX2mCpVwLEE8qXP2uSkEv9vBZ994Nu962ZK8ZZrLOwIsb/fz0I6TfO33+xmLxvnxX72M5/7fNfzwfZfwyrM6C3rNJq+TO957MZ2Nbo4MhHnfFctp9royfqOy3nslhN66kEhEL1QLpX9fr3Ja/S6SGnNcXeak31gkjtthw+Uo/Ho40cEynjWZeHx4nGg8ycJmb85zndnZAMCek2PMa/BkPMaYLpX7f2N3ixenXbGvb7rQp0b/lSn0AK84q5NXFCjS157VyXeePMDGg4PccO7ClFUzrzHz+8xGR4ObO993KfdtOc7rz+/isd29nBiZXlljVedYw8vLwbJuxKMXqoW6jegL2TQ1WkT7AwtLMHN5wZsPDwFw3qLmnOdaZQr97pOjWY8JRjLPi03HYbextM3P3t6xaY89vsdoEX2G+Vqni2tWzyOW0EQTST587Rllnaur2cstV67A5bDR4J5e9aS1TvPoyxdn69uaVN0I1ULdCr3V5zzXpqlixghanNFpbPLZemx6iZ/F5iODuBw21izInXBsD7ho8TlzCn2oAOsGYOW8APv6pgv9j54/wooOPxcszn3RqTQXLmlhQZOHt128iKXmIJFK0OidvmHt1FiUYNRoOTEcKmymby4sj16sG6FaqF+hL6DfzVgBm5qmsrjVR2ejm+cOTGvBn2LzkSHWLmzMawkppVjV2cDuk9MF2iIUzZ/UBVg1L8Ch/uCk3jB7To7ywuEh3nrR4oLaH1QSh93GQx95OZ++YV1Fz9vocTIeS04aQG5F82d3NxFNJPNWMuVDrBuh2qhfobc6WOYS+hIieqUUFy9r47kDAxkjx1giydZjw3ltG4szOxvYfWI0axQaimaeLjWVFfMCJPXknaQ/ev4IDpviDTl2oM4kfrcDe45qoVKwciTpyXCr4ua8RS1AefZNPJFMdQyViF6oFupW6Fssjz6HdWP0oi8+SXnxslZOjIxzJMMIv10nRhmPJQsW+jM6A4xG4hkTjJB9XuxUVs4zLKU9ZiOyaDzJPS8e4xVnddLdKmAfAAAgAElEQVSeo3lYtZGaCZC2ae3gqSB2m2Jdl2GVlbNpKr2vfyGTxARhLlC3Qm/1u8m1O3YsEis6GQtwybJWAJ49MH2b/+YjRiL2fDO6zMdEQjazfROK5k/GAqzoCKAUqYTswztOMhCM8uaLFhW0jmohNc4xnB7RB+lu8aYuaOWUWKbvepaIXqgW6lbowWqDUFnrBmBlR4AWnzOjT7/5yBCtfleqfXA+rGqY3ScyJ2SDkcKSsR6nne4Wb0rof7XlOB0Nbq5cdXrn9c40qfLWNOvmUH+IJW3+1I7dSgi9w6YKFvpP/nIb33/6YMmvKQjlUtdC3+p3ZbVuUpuaSojobTbFRUtbee6g4dN/6t7tvOX/nubkyDibjwxx3qLmgpOfrX4X7QF3xsqbZFITjuWvo7dY2RFgb+8Y4WiCR3f28aq1nRX3yGebiYjeEGStNQf7gyxt86WarZXTBsHy/hc0ewpOxv5m6wke29VX8msKQrmI0GeJ6CPxJLGELimiB8OnP9Qf4tZ7tvLdpw6y8dAgr7/tSfb1jRXsz1uc0RnIKPThWO4WxVNZOS/A/lNBHt3VSziW4Lp1C4paRzVgWW1WRD8YijE6HmdJm7+o4e3ZsCL67mZfQedJJjWDoWhFeuwIQqnUtdC3BdxZPfpSetGnc8myNgDufv4Ib7t4Eb/8wGUktUbr/BulpnJGZwN7esemtWsIWkNHCrwYrZwXIBpP8vU/7KfF50zlEmqJiZ3JhggfNEsrl7b58DjteJ12BgsYCp+NUbOjaVeLl0g8mbeN9Oi40fdoSPx8YRap2xYIYHj0g6EYyaSe1hRsrITpUumsWdBAR4Ob8xc18/+9bh0Ou41ffOAyHtx2gg0r2oo+Vyia4Jn9/WxY2Z66P2yNESwiogcjT/Dm9d047LV3nfe77NjURORt1dAvMQeVNPucZYluKqJvMXIsI+MxPDnKWwfMSL4SPXYEoVRq7y+9CFr9LhJJzfHhMH/5/Y3c8fTB1GOldK5Mx2G38ehHr+Jrf35hSlAXNHl5z2XLihbYPz13IYtbfXz8nq2pYeCQf7rUVFZ2TLQ5qEXbBox9DI1eZ8q6OT5klKV2NVtC76pIMrbL7FOUr4Ol1WJjKE8DPUGYSepa6K3dsX/xned5cPtJ/uuhPakdldYEpOUdpW/PD7gdOdsHF4rP5eALbzqHwwMhPn//ztT94VjuebFTafI56Whw0+B2sGFlcd8qqolGz0S/m+NDYVp8ztQIw2avs6w6+tHxOC67jY4Go1Qzn08/EDQeT2pjX4YgzAb1LfRmv5s9vWO8Zf0iBoJRHtpxkmRSc+ezh7hoaQsr553eZl/ZuGR5G+/ZsJTvPX2ITYeMsk0rovfnGWCSzhsv6OLmK5bjdhT+nGrD6HdjiGrP8DgLmiZKWVv85Vo3MQIeRyqxm6/yJj0fUInpVoJQCnUt9Ms7/Phddv7t9ev4zBvPZmGTh7ufP8JT+/o52B/iHZcsme0lTuJjrzoTgCf3Gt82LBvH6yzcXrr1ujV86NpVlV/cHGJqRL+weaL1cZPXVXZE3+BxZKzXz0T/JKEXn16YHeo6Gbuw2cuWT70qVUt+4/pFfPWRPYyNx2jxOXn1uvmzvMLJ+N0OAm5HSjCsnivFRPT1QKPHyf5TxsawnuFxLlo6UV3U4nMyZHawLKWR25jZurrQUs30skopsRRmi7qO6IFJG4ZuXN8NwAuHh3jz+kU5qylmi2bfhMecmhdbYDK2XmjwOBgJxwlF4wyHYyxIi+ibfU7iST2pZ00uEknN9585lLJgRsdjNLidGVstZGJAInphDlD3Qp9Od4uPy83yxbddvHiWV5OZFp8rFRmGTevGX2Aytl6wqm6sipuFaR69NfWrUNH99ZbjfOIX2/jF5mPAhHXjctjwOu0FJGOjLGzymK8pEb0wO4hCTOFf/vQsth0bqegwjErS7HOm2uxaydhC2hTXE40eJ6FogiODRnviBU1pEb13ot/Nojz7xRJJzX8/shcw+uWAIfRWW4wmr7MgoV/a7uf48HhFplsJQimI0E9h5byGOVNpk4lmn4sjA4bohKJxvE57RUo4awmrVbHVCC59Nm8qoi9gdux9W3vY2zuGw6Y4bH7mI+OxlG3T6HXkraMfDEVZ0tZMo8chEb0wa4h1U2W0pEX0Ri96ieanYgnxrhOjKAWdaQPHWwrsYJlMav774T2smhfgT1bP41B/MNXorqGYiH4sSovPZWzUqsM2CA+9dDK1g1uYPUToq4xmn4uR8RiJpDamS0kidhpW6ePOE6O0B9yTRjY2pYQ+d3T9ux0n2dM7xgevWcWydj9HBsOMReJoPdH/qNHjzFleGY0nGY3EafW7Jl2g64XjQ2FuvmMj923tme2l1D0i9FVGi8+J1kZZXygal0RsBhpNId7bN5ZKhFo0ewtLxj5/YAC3w8Zrzl7A4lYf0Xgy1cu/wfzGkC+ity4mrX4joh+uM+vGugiO5tlrIMw8IvRVRnpP9VC0sKEj9YYV0UfjyUm7YgFcDht+lz1vdL2nd4yV8wLYbSrVEG3b8REgLaL3OnOWV1qbpQyhr7+I3trnUe4wdqF8ROirjPQpScFIvOA+N/VEemvp9Bp6C8Mvzx1d7+0dY5XZ7XNJq1GBtf3YMDDR6K7R62Q0Es/arMyqvW/xuSaVxdYLljc/Lh79rCNCX2VM1IFLRJ8NK6KHyTX0Fs3m7thsjEXiHBsKp+b1Lmj2YLcptqci+gnrRuvJc2TTsVoUtwWMiH50PE48kSztTVUhVkQfEqGfdUToqwyramQwFBOhz0LA5cDqbpApom8LuOkdHc/6fMuLtyJ6p91GV7OXXWa5ZmMqGTt5mtVUBtIi+uYKTLeqNqxeTGLdzD4i9FXGtIi+xH75tYzNpmgwP5epHj3A6vkN7D45RixLdL3HHNtoRfRgDC6JmsenR/QwWbwTSZ1KPlpC3+xz0uK3civ1I/ThAj36D939Il/67a7TsaS6RYS+ymj0OLDblJmMjRc8XaresOybhRki+nVdTUTjyYxzeMFIxLocNha3+lL3LUr7OT0ZC5P73Xzj8f1c8R+PEorGGQxGafI6cdptqQv0cAEbtaqV+7f28NvtJ1K3Lcsm37jFFw8PscXMfwgzQ16hV0p5lFLPKaX+qJTarpT6tHn/NUqpF5RSm5VSTyilVpr3u5VSP1JK7VVKPauUWjqzb6G+UErR7HUyEIyZdfQS0Wei0ePEblPMa5gu9Gd3NQGwLYu47Dk5yoqOwKSGd0tMobepiYleVkSfvhHqvi09DIVi/GH3KfqDUVrNSN6ybgaDtRvR3/77fXzz8QOp21Ykn2/DVCiaIChDWWaUQiL6CHC11vpc4Dzg1UqpS4HbgXdorc8Dfgj8s3n8TcCg1nol8GXg85Vfdn3T7HPSMxwGCp8XW280eh10NrgnibXFklYfAbeDrdmEPq3iJvUcs8Qy4Hak2hsvavXhdth4dr8xH6B3ZDx1zt+9dJLBUDSVU2lJtV6oXaEfi8QndQUt1KMPReOpvk3CzJBX6LXBmHnTaf7T5r9G8/4m4Lj58+uA75k//xS4RpXS+FvISovPxbFBQ+jFo8/M5SvbeeXazPMEbDbF2oWNbD02Mu2xUDTO0cHwNKG3rBvLnwdD9K89q5Nfbekhlkjy2K4+AM5a0MgjO0/SNxpJRfSF7sitZoKROMFoutDnj+iTSU04lpg0C1moPAV59Eopu1JqM9AL/E5r/SxwM/AbpdRR4J3A58zDu4AjAFrrODAMTBtQqpS6RSm1USm1sa+vr/x3Ukc0+1wcHzKFXjpXZuRvrl7Fp25Ym/Xxs7ua2NEzMi0hm6q46Zwa0Ru19Ok1+gBvOK+LgWCUx/f08fDOkyxo8vCBP1nJYCjG7pNjKaFPz63UKsFIYlJkXkgydjyeQOuJ2QrCzFCQ0GutE6ZF0w1crJRaB3wYeI3Wuhv4DvAl8/BM0fu0HSVa669rrddrrdd3dHSUtvo6pdnnTP1hSFOz0ji7u2lSWwOLPSctoZ/cwTTgdtDmd6UapllceUYHzT4nP37+KE/sOcWfrJ7Hy8/swGU3/rSsahsrt1Krw0e01gSj8UleeyE7Y61jxKOfWYqqutFaDwGPAdcB55qRPcCPgA3mz0eBRQBKKQeGrTNQicUKBpbvC0gytkTWmQlZy1PvHR1nIBhlT+8YTrtKJV/TOX9xM8s7Js8pcDlsvPacBTyw/QTBaIJrVs8j4HawYaXxJbbV9ObBsG9qVehDUSMyD8cSJMydwhPWTfZNYuG0TVXZdhgL5ZNXJZRSHUBMaz2klPIC12IkWJuUUmdorXcDrwB2mE+5F3g38DTwJuARrbX8H6wgzWniIcnY0ljW5ifgdrDt2DBNXifv/8EmktoYLbmyI4DDPj0G+r93rs/4dfUN53fxg2cO43bY2LDCmFD2irM6eWxXX8q6AWq6DUJ6RB6Mxmn0OAnHjPtylVeme/rhWAK/5JxmhEI+1QXA95RSdoxvAD/WWv9aKfU+4GdKqSQwCLzXPP5bwPeVUnsxIvm3zsC665qWNKGXXjelYbMpzlrYyEMvneRHzx/hnO5mrj97AbtOjqbGSU4lUwUPwAWLW1je7mfFvECqbfR16xbwm609rJ8ymPz40Dj3benhs/fv4L/fdj7nL26p/JubBdKrbYKReGrKFxgCnm0Ye3p7hGA0LkI/Q+T9VLXWW4DzM9z/c+DnGe4fB26syOqEjKRbN9ICoXTO7mriuQMDLG3z8a13r6ct4C7pPEopfvxXL5vU977V7+LOmy+ddFyT18Xvd/fxgR++AMBtj+7jm+9eX/obmEOkJ2Gt6N6yZRJJTTSRxO2Y/rsamvS8BMzd4W5VjVw+q5B068YnydiSuXZNJ88e6Oe2t19QsshbtBfw/Fa/k1hCc/05C+hu8fL1P+xnf98YyzsCeZ8715kc0U9vZjYezSL00cnfBISZQYS+Cmnxp0f08r+wVF62oo1f/+0Vp+313nHJElZ0BHjz+kX0B6N854mDfOfJg3zqhrX8y73beGDbCc5f3MI1q+fxlosWZbQ65irByHTBDkUT2BQkzSRtE85pz0uvyJEulzOHqEQVYk1JAvBKHX3VsLTdz9J2o2qno8HN689fyE82HaFvNMID209w1Zkd7OgZ4XcvnaTZ5+TV6xbM8ooLJz2pOpaybuK0+Fz0B6NZSywnWT6yaWrGkKZmVYg1fMTrtGdNEApzn5suX854LMkD209w63Wr+e5fXMyjH70Kv8vOE3tPzfbyimJsStWN1ppQLEFbwAhKsu2OTbduQtIGYcaQiL4K8TjteJ12ScRWOWfOb+CjrzyD+U1e3nRhN2D0vr9keRtP7e2f5dUVR7p1MxZJEIkn0ZpUeWm2iH5S1Y149DOGRPRVSovPKYnYGuBvrl6VEnmLDSva2H8qmGpcl4lP/2o7j+w8OdPLK5ixKVU3loC3+Y0kdfaIXqyb04EIfZXS7HPhl0RsTWJturKi+l0nRnlgW0/q8V0nRvnOkwf570f2zsr6MhGMxPE67ShlCb0h2vkj+njqm6kkY2cOEfoqZWGzN+V/CrXF6vkNtPicPLWvn1giyV/fuYkP/PBFjpmN7H65+RhgDOywmtvNNsFInIDHgd/lIBhJpCL4QqybZq8Tp12JdTODiNBXKZ954zq+eON5s70MYQaw2RQvW9HGU/tO8YNnDrGvL0giqbnj6YMkk5pfbj6eaqN8/7YTuU92mhiLxAm4Hfjd9knWTbsZjIxnidbD0QRelx2fyyFCP4OI0Fcp8xo8zG+aPj1JqA02rGinZ3ic/3hgF5evbOe6dfO569nDPLH3FMeGwrz/qhWsnt/Ab7b25D/ZaSAYieN32/G7HYxFJ4S+1fLos5VXmm0PAm6HtCqeQUToBWEOsmGF0f0yEk/wz69dw02XL2NkPM5Hf/JHPE4br1w7n+vPXsCmQ4P0DIc5NRbhJxuPzFoHyGAkgd9lCnYknmpoZm3uy+a/h6KJVAWZDB+ZOSSbJwhzkGXtftYsaGTDijZWz29Ea8053U1sOTrMa89ZQMDt4DXnLOCLv9vNfz64myf29nFyJEJXs5cNWZqyzSRjkTgLmjzYlJpk3RSSjO0IuBmPO2Sc4AwiEb0gzEGUUvzmg5fzz9evSd2+6fJlALzxgi4AVnQEWD2/gZ+9cBSHzfhT3jNlkMrpIhg1k7FuO2ORREro/S4HHqcta6viUDSBz+3A77KLRz+DSEQvCHOUqb1ubjh3Icva/ZxtDk0B+NirzuSpff383bWr2PDZR6ZNzDpdGB69ISehaDxVdeN1GZv7stXRh6MJfE47EZeDwdDcqCCqRUToBaFKUEpxTnfzpPuuWdPJNWs6AVjZGWBP7+hsLC1VdQOTN0z5LKHP2uvGuEDEEknx6GcQsW4EoUZY2RFgb2/wtL9uPJFkPJZMJWPHInHCpmh7HHa8ruxCH46Z5ZVu8ehnEhF6QagRVnUGODUWYeg0jyu0BNrvtuN3ORiPJRk1d8rabMoQ+gzWTTSeJJbQ+F12/FJ1M6OI0AtCjbDS3ER1un36MVOgrQ1TAKfGoqnWBtk8+gkf34HP5ZAB4TOICL0g1Air5hlz+E535Y1VLeN3O1IJ2b7R8dT8XE8Wj95qYuZz2VP+fijHIHGhdEToBaFG6Gr24nHaTn9EH0mP6C2hj0yK6DOVV6YnbK1OrCEpsZwRROgFoUaw2RQrOgKzGtEH0qwbr9ld1ZclGRtOCb0j1YlV2iDMDFJeKQg1xMp5ATYeHDytrzkh9Ha0Njz24XAMnznmMlsy1rJu/K6J58mmqZlBInpBqCFWzQtwbCh8WgXTGjqSbt0AKevGkzcZa59Ugy9UHhF6QaghVpoJ2X19p8++mWzdTAi9N73qJkcy1u924EvtqhXrZiYQoReEGsIqsdx98vQJfXoyNn28ZXoyNp7UxBLJSc+zRN3rNOroQcYJzhTi0QtCDbGkzYfHaeOffr6V+7f2sHJegJ7hccZjCT77xrNpCxj94Z/ce4rhcIzXnL2g7NcMRuLYbQq3wzYpoveZCVYrsg/HEjjtE7FlOK3qxmZTqXMJlUeEXhBqCKfdxl3vu5Rfbj7OQztO8vieUyxo9nB8KMwnf7md295xAYf7Q9xyx0Y0cNWZHSlBLpVgJI7fZUcpZeyGVZDUadaN+d/xaIJGj3PieWnWjcOWNM8l1s1MIEIvCDXG+YtbOH9xC5+6YS1aa5RS3PboXr7w4C5eufkYdzx9iFhCE00k+e32k7z+/K6yXm8skkhF8kop/C4Ho5H4RNWNcyKiTyccTaAUuB02bGanTmmDMDOIRy8INYzV6vgvr1zOud1NfPhHm9l0aJD/eNM5dLd4uefFY2W/RnqLYiD1c3oyFqYnWq2pVEopXA4bLrtN6uhnCBF6QagDHHYb/3njuTjtNl5/3kJef34Xbzi/iyf29NE7Ml7Wua25rxZWvxvLEvK4skT0sXjqYgDgc9tlZ+wMIUIvCHXCqs4Gnvz41XzpzecB8Ibzu0hq+OXm42WdN70XPUxE9FbVjWXhjGeM6CeE3u9ypGryhcoiQi8IdUR7wJ2qcFneEeDcRc387IWjnBwZJz6l/LFQDOtmsmDD9GTs1Ig+FE2k2iSA8U1APPqZQYReEOqYN13Qxc4To1zymYc58xMP8A8//SPDoVhR5whGEhk9ep8rTzI2Fp8U0ftcDvHoZwipuhGEOuatFy+mq8XL8aFxdvSMcPfzR3hsVx83X7GMzkYPy9r9k8YXHhkIkdSaJW3+1H1TrZtAyqOfaIEATGuDEIwkaPBMiejFo58RROgFoY5x2m1cvbozdfutFy3mH3+2hc/8Zmfqvt9/7KqUsH/w7hc5NhjmkY9eRcDtQGudverGOX3DVDrhaILORnfqts/loH8sVOF3KIBYN4IgpHF2dxP3ffByNv3ztXz/posBeGpfP2BE7luODtM7GuG2R/cCEIkniSf1lIh+SjLWlSWij8ZTfr71POl1MzOI0AuCMAmlFG0BN5evbKc94ObZ/YbQbzo0SCKpWdHh51uPH+DAqSC/3tIDMMWCmSz0Hkf2iH5SeaXMjZ0xxLoRBCEjSikuWd7KswcG0Frz7P5+HDbFt959Edd/9XHedPtT9AejXLikhdeeszD1vOUdflp8Thq9RrsDm9kHJ1PVzVTLZygU4023P8We3jHeeEEXf3ftGTR5nQjlkVfolVIe4A+A2zz+p1rrf1HGlrt/A24EEsDtWuuvmvd/BXgNEALeo7V+YabegCAIM8ely9u4b0sPhwdCPHtggLO7m1ja7ufvX3kmn71/Bx971Zn81ctXYDdLNgGuP3sB161bMOk+r8s+qY4+mdSEY4lURQ4YvfRtNkVSay5b2cZ3nzrIr/54nAsWt3ByNEIsnmRZh5+zFjTyF5ctLbtHTz1RyCcVAa7WWo8ppZzAE0qp+4E1wCJgtdY6qZSaZx5/HbDK/HcJcLv5X0EQqoxLl7UC8NiuPrYcHeKmy5cD8N7Ll/H2SxanKmrSUUphV5Pvm9qT3vo5vf7+xvWLeNOF3am2DduODfO5+3dysD9IZ6MHu02x7dgw923p4eCpIF+48dyKvtdaJq/Qa2PGl9Xc2mn+08D7gbdrrZPmcb3mMa8D7jCf94xSqlkptUBr3VPx1QuCMKOsnBegze/iG4/vJ5bQXGIKP5BR5LPhddkJRhOpJmtW50rvlKjcEnmAdV1N/ODm6THifz64i/95dC/XrOnk1evmF/uW6pKCvvsopezAJmAlcJvW+lml1ArgLUqpNwB9wAe11nuALuBI2tOPmveJ0AtClWH59L/ZegKbgvVLW0o6T8Dt4L4tPdy3pQebImXrpG+YKpQPXbuK3+/u4//9fCvndDexsNlb0prqiYKEXmudAM5TSjUDP1dKrcPw7Me11uuVUm8Evg1cAahMp5h6h1LqFuAWgMWLF5e4fEEQZppLlrXxm60nWLuwiQZPaYnRT7z2LJ7Z1088qUlqTTypcdgUV6+el//JU3DabXz5Ledy/VefYMPnHmFhk4ezFjZxxap2Ll7WSiyRZCgU49xFzZLINSkqm6G1HlJKPQa8GiNS/5n50M+B75g/H8Xw7i26gWldk7TWXwe+DrB+/fppFwJBEOYGly5vA5hk2xTLRUtbuWhp6c+fysp5Ddzz1xv4/e4+dp8Y5YXDQzy04+SkY5a0+fjh+y6lSyL+gqpuOoCYKfJe4Frg88AvgKsxIvmXA7vNp9wL/I1S6m6MJOyw+POCUL2c0Rng49et5voKjB2sJGsXNrF2YVPq9qH+IJuPDOFzOYglkvzjz7bw5q89zQ/fd8mklg31iDJypjkOUOoc4HuAHWOD1Y+11v9q2jh3AosxkrV/pbX+o1le+T8YUX8I+Aut9cZcr7F+/Xq9cWPOQwRBEIpi27Fh3vmtZ/G5HDz44Ssn7d6tFZRSm7TW6/Mel0/oTwci9IIgzASbDg3wpq89zbtftpRP3bB2tpdTcQoVemmBIAhCzXLhklbeeekSvvf0QV48PDjby5k1ROgFQahpPvaqM+ls8HDrPVuJxrMPVxkKRbn1nq08urM36zHVigi9IAg1TYPHyb++bi07T4xyx9MHMx6z/fgwf/o/T3DXc4d5/52b2HZs+LSucaYRoRcEoeZ5xVmdXHlGB195eA/9Y5FJjz300kne+L9PEYtrvvmu9bT6XLzvjo1lD02fS4jQC4JQ8yil+MT1awhFE3zpd7tT9/96y3H+6gebWD2/gV/97eVce1Yn33j3eoZCMT5494vMhWKVSiBCLwhCXbCqs4F3XrqEu547zJd+u4uP/eSPfPCuF7lgcQs/uPkSOhqMaVdrFzbxj68+k2f2D7DxkJHAjSWS3HrPFp42h7AA7Osb4+9//EfGY3N/WIoIvSAIdcPfXbuKVr+Lrz6yl0d29nL9OQv57nsvmtba4S0XLabF5+T/fr8fgO89dZC7njvCrfdsIZYwErr/+quX+NkLR3lq36nU84bDMQaD0dP3hgqk9nYQCIIgZKHZ5+LRj16FUirnBiqvy847L13CVx/Zy7P7+/nKQ3vobvFysD/ETzcdZVm7n9/v7gPgqb39qbm7f3vXixwdCPHbD1+Jwz534ui5sxJBEITTQIPHWdAu2XdtWIrbYeM933me8XiC7733Ys5f3MxXHtrD5+7fyfxGDxcuaeFJ084ZDEZ5cu8p9p8K8vMXj8302ygKEXpBEIQMtAfc/NmF3YRjCd572TJWdAT42KvO5MTIOJuPDPHBa1bxJ2d2sKNnhIFglEd29pJIajoa3Hz1kT0pi2cqA8Eoj+46vbX6IvSCIAhZ+ODVq3jPhqX87TWrANiwop2rzuxg5bwAN67vZsPKdgCe3tfPb186wfxGD599w9kcGQhzzwtHGY8l2HRokLGIMWhlMBjlrV9/mr/4zvPs6Bk5be9DPHpBEIQszG/yTOuR87U/v5BEUuO02zinq4mA28HDO0/y+919vHn9Iq5ZM49zu5v4t1/v4F/u3c54LEl7wM2HrlnJTzYd5WB/CKWM+v01CxpPy/uQiF4QBKEIPE47ftPjd9htXLKslV+8eIzxWJJXnjUfpRT/dP1ZLG7z8Zb1i/jyW85laZuPT/xyOy8dH+Frf34B5y1qntY/fyaRiF4QBKEMNqxs5+GdvTR4HFyy3BiucvGyVu774BWpY15/XheP7OzF53LwshVt7OgZ5QsP7uLkyDidjZ4ZX6NE9IIgCGVw2UpjAtfVq+fhzFJSqZTimjWdvGyFcey1a4xyzId3nJ6krAi9IAhCGZzZ2cAtVy7nliuXF/ycMzoDLGr1njb7RqwbQRCEMlBK8f9es6bo51y7ppM7nz1MKBrH55pZKZaIXhAEYRZ4xZpOovEkj+85lf/gMhGhFwRBmAUuWtbK1avn4XXaZ/y1xLoRBEGYBZx2G99+z0Wn5bUkohcEQRrOytQAAAdoSURBVKhxROgFQRBqHBF6QRCEGkeEXhAEocYRoRcEQahxROgFQRBqHBF6QRCEGkeEXhAEocZRWuvZXgNKqT7gUIlPbwdmfg/xzFCta6/WdUP1rr1a1w3Vu/ZqWPcSrXVHvoPmhNCXg1Jqo9Z6/WyvoxSqde3Vum6o3rVX67qhetderevOhFg3giAINY4IvSAIQo1TC0L/9dleQBlU69qrdd1QvWuv1nVD9a69Wtc9jar36AVBEITc1EJELwiCIOSgqoVeKfVqpdQupdRepdTHZ3s92VBKLVJKPaqU2qGU2q6U+pB5f6tS6ndKqT3mf1tme62ZUErZlVIvKqV+bd5eppR61lz3j5RSrtleYyaUUs1KqZ8qpXaan/3Lqugz/7D5u7JNKXWXUsozFz93pdS3lVK9Sqltafdl/IyVwVfNv9ctSqkLZm/lWdf+BfP3ZYtS6udKqea0x241175LKfWq2Vl1aVSt0Cul7MBtwHXAWcDblFJnze6qshIH/l5rvQa4FPiAudaPAw9rrVcBD5u35yIfAnak3f488GVz3YPATbOyqvx8BXhAa70aOBfjPcz5z1wp1QV8EFivtV4H2IG3Mjc/9+8Cr55yX7bP+DpglfnvFuD207TGbHyX6Wv/HbBOa30OsBu4FcD8e30rsNZ8zv+aGlQVVK3QAxcDe7XW+7XWUeBu4HWzvKaMaK17tNYvmD+PYghOF8Z6v2ce9j3g9bOzwuwopbqB64FvmrcVcDXwU/OQubruRuBK4FsAWuuo1nqIKvjMTRyAVynlAHxAD3Pwc9da/wEYmHJ3ts/4dcAd2uAZoFkpteD0rHQ6mdautf6t1jpu3nwG6DZ/fh1wt9Y6orU+AOzF0KCqoJqFvgs4knb7qHnfnEYptRQ4H3gW6NRa94BxMQDmzd7KsvJfwD8ASfN2GzCU9scwVz/35UAf8B3TdvqmUspPFXzmWutjwH8ChzEEfhjYRHV87pD9M662v9n3AvebP1fb2idRzUKvMtw3p0uIlFIB4GfA32mtR2Z7PflQSr0W6NVab0q/O8Ohc/FzdwAXALdrrc8HgsxBmyYTpqf9OmAZsBDwY9geU5mLn3suquV3B6XUP2FYrndad2U4bE6uPRPVLPRHgUVpt7uB47O0lrwopZwYIn+n1voe8+6T1ldX87+9s7W+LFwG3KCUOohhjV2NEeE3m5YCzN3P/ShwVGv9rHn7pxjCP9c/c4BrgQNa6z6tdQy4B9hAdXzukP0zroq/WaXUu4HXAu/QE/XnVbH2bFSz0D8PrDIrEVwYiZJ7Z3lNGTF97W8BO7TWX0p76F7g3ebP7wZ+ebrXlgut9a1a626t9VKMz/cRrfU7gEeBN5mHzbl1A2itTwBHlFJnmnddA7zEHP/MTQ4DlyqlfObvjrX2Of+5m2T7jO8F3mVW31wKDFsWz1xBKfVq4B+BG7TWobSH7gXeqpRyK6WWYSSUn5uNNZaE1rpq/wGvwciM7wP+abbXk2Odl2N8zdsCbDb/vQbD734Y2GP+t3W215rjPVwF/Nr8eTnGL/le4CeAe7bXl2XN5wEbzc/9F0BLtXzmwKeBncA24PuAey5+7sBdGHmEGEbUe1O2zxjD/rjN/HvdilFVNNfWvhfDi7f+Tr+Wdvw/mWvfBVw32599Mf9kZ6wgCEKNU83WjSAIglAAIvSCIAg1jgi9IAhCjSNCLwiCUOOI0AuCINQ4IvSCIAg1jgi9UJWYLYj/Ou32QqXUT3M9p4zXer1S6pNFPuc36S1usxzzn0qpq8tbnSDkR+roharEbA73a2208Z3p13oKY6fkqQqfdwnwDa31Kyt5XkGYikT0QrXyOWCFUmqzOSxiqTVAQin1HqXUL5RSv1JKHVBK/Y1S6iNmF8tnlFKt5nErlFIPKKU2KaUeV0qtnvoiSqkzgIgl8kqp7yqlblfGIJn9SqmXmwMsdiilvpv2vINKqXZzXTuUUt9QxiCR3yqlvABa60NAm1Jq/sx/XEI9I0IvVCsfB/Zprc/TWn8sw+PrgLdj9Az/dyCkjS6WTwPvMo/5OvC3WusLgY8C/5vhPJcBL0y5rwWjwduHgV8BX8YYSHG2Uuq8DOdYBdymtV4LDAF/lvbYC+ZrCMKM4ch/iCBUJY9qY8jLqFJqGEOQweixco7ZMnoD8BOjbxhg9JOZygKMvvbp/EprrZVSW4GTWuutAEqp7cBSjB4p6RzQWlv3bTKPsejFaEUsCDOGCL1Qq0TSfk6m3U5i/N7bMAZ5ZIrA0wkDTVnOnX7e9HPnWksC8Kbd9pivIQgzhlg3QrUyCjSU+mRtDH45oJS6EVKDq8/NcOgOYGWpr1MAZ2B0qBSEGUOEXqhKtNb9wJNKqW1KqS+UeJp3ADcppf4IbCfzzOE/AOerNH+nUpjDaFZitFIWhBlDyisFIQ9Kqa9g+PIPVfi8bwAu0Fp/opLnFYSpSEQvCPn5DOCbgfM6gC/OwHkFYRIS0QuCINQ4EtELgiDUOCL0giAINY4IvSAIQo0jQi8IglDjiNALgiDUOP8/n/8nPzA5naoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = df_mat['cfpNuc'][25, 5:]\n",
    "plt.xlabel('time (min)')\n",
    "plt.plot(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}